"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " [1/15] Nature - Drug Discovery: 30 documents\n",
- " [2/15] Nature - Pharmacology: 30 documents\n",
- " [9/15] Labroots Health & Medicine: 30 documents\n",
- " [11/15] PLOS ONE - Medicine: 30 documents\n",
- " [12/15] PLOS Biology: 30 documents\n",
- " [13/15] PLOS Medicine: 30 documents\n",
- "Ingested 180 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.ingest import FeedIngestor, FileIngestor\n",
"import os\n",
@@ -288,27 +232,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Normalizing 180 documents...\n",
- " Normalized 50/180 documents...\n",
- " Normalized 100/180 documents...\n",
- " Normalized 150/180 documents...\n",
- " Normalized 180/180 documents...\n",
- "Chunking 180 documents...\n",
- " Chunked 50/180 documents (50 chunks so far)\n",
- " Chunked 100/180 documents (101 chunks so far)\n",
- " Chunked 150/180 documents (194 chunks so far)\n",
- " Chunked 180/180 documents (250 chunks so far)\n",
- "Created 250 chunks from 180 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.normalize import TextNormalizer\n",
"from semantica.split import TextSplitter\n",
@@ -356,31 +282,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting entities from 250 chunks...\n",
- " Processed 20/250 chunks (11 entities found, 230 remaining)\n",
- " Processed 40/250 chunks (27 entities found, 210 remaining)\n",
- " Processed 60/250 chunks (47 entities found, 190 remaining)\n",
- " Processed 80/250 chunks (64 entities found, 170 remaining)\n",
- " Processed 100/250 chunks (154 entities found, 150 remaining)\n",
- " Processed 120/250 chunks (312 entities found, 130 remaining)\n",
- " Processed 140/250 chunks (502 entities found, 110 remaining)\n",
- " Processed 160/250 chunks (669 entities found, 90 remaining)\n",
- " Processed 180/250 chunks (873 entities found, 70 remaining)\n",
- " Processed 200/250 chunks (1105 entities found, 50 remaining)\n",
- " Processed 220/250 chunks (1478 entities found, 30 remaining)\n",
- " Processed 240/250 chunks (1967 entities found, 10 remaining)\n",
- " Processed 250/250 chunks (2195 entities found, 0 remaining)\n",
- "Extracted 26 drugs and 509 proteins\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import NERExtractor\n",
"\n",
@@ -421,31 +325,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting relationships from 250 chunks...\n",
- " Processed 20/250 chunks (25 relationships found)\n",
- " Processed 40/250 chunks (43 relationships found)\n",
- " Processed 60/250 chunks (63 relationships found)\n",
- " Processed 80/250 chunks (109 relationships found)\n",
- " Processed 100/250 chunks (155 relationships found)\n",
- " Processed 120/250 chunks (365 relationships found)\n",
- " Processed 140/250 chunks (577 relationships found)\n",
- " Processed 160/250 chunks (773 relationships found)\n",
- " Processed 180/250 chunks (1018 relationships found)\n",
- " Processed 200/250 chunks (1251 relationships found)\n",
- " Processed 220/250 chunks (1478 relationships found)\n",
- " Processed 240/250 chunks (1730 relationships found)\n",
- " Processed 250/250 chunks (1848 relationships found)\n",
- "Extracted 1848 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import RelationExtractor\n",
"\n",
@@ -497,51 +379,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detecting conflicts in 2195 entities, 1848 relationships...\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Type conflict detected: 40 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: bariatric conflicting types: ['PERSON', 'GPE']\n",
- "Type conflict detected: the Switch Phase conflicting types: ['FAC', 'ORG']\n",
- "Type conflict detected: 3′ conflicting types: ['CARDINAL', 'ORG']\n",
- "Type conflict detected: Chinmo conflicting types: ['ORG', 'PERSON']\n",
- "Type conflict detected: 15 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: CDKL5 conflicting types: ['PERSON', 'GPE']\n",
- "Type conflict detected: 50 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: BRCA1 conflicting types: ['CARDINAL', 'NORP', 'PERSON', 'GPE']\n",
- "Type conflict detected: Cezanne conflicting types: ['PERSON', 'GPE']\n",
- "Type conflict detected: HCAR2 conflicting types: ['ORG', 'GPE']\n",
- "Type conflict detected: HCAR3 conflicting types: ['ORG', 'GPE']\n",
- "Type conflict detected: 166 conflicting types: ['CARDINAL', 'MONEY']\n",
- "Type conflict detected: PrEP conflicting types: ['FAC', 'PERSON', 'ORG']\n",
- "Type conflict detected: 5 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: CI conflicting types: ['WORK_OF_ART', 'ORG']\n",
- "Type conflict detected: SAM conflicting types: ['WORK_OF_ART', 'ORG']\n",
- "Type conflict detected: 1.57 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: Gavi conflicting types: ['PRODUCT', 'PERSON']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detected 19 entity conflicts, 0 relationship conflicts\n",
- "Resolved 19 entity conflicts\n",
- "Conflicts resolved. GraphBuilder will use cleaned data.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.conflicts import ConflictDetector, ConflictResolver\n",
"\n",
@@ -602,26 +442,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n",
- "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating embeddings for 26 drugs and 509 proteins...\n",
- "Generated 26 drug embeddings and 509 protein embeddings\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.embeddings import EmbeddingGenerator\n",
"from semantica.vector_store import VectorStore\n",
@@ -654,18 +477,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Storing 26 drug vectors and 509 protein vectors...\n",
- "Stored 26 drug vectors and 509 protein vectors\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"print(f\"Storing {len(drug_embeddings)} drug vectors and {len(protein_embeddings)} protein vectors...\")\n",
"drug_ids = vector_store.store_vectors(\n",
@@ -692,67 +506,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building graph from 2195 entities, 1848 relationships...\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Processing 2195 entities, 1848 relationships...\n",
- " Processed 500/2195 entities (1695 remaining)\n",
- " Processed 1000/2195 entities (1195 remaining)\n",
- " Processed 1500/2195 entities (695 remaining)\n",
- " Processed 2000/2195 entities (195 remaining)\n",
- " Processed 2195/2195 entities (complete)\n",
- " Processed 500/1848 relationships (1348 remaining)\n",
- " Processed 1000/1848 relationships (848 remaining)\n",
- " Processed 1500/1848 relationships (348 remaining)\n",
- " Processed 1848/1848 relationships (complete)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Type conflict detected: 40 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: bariatric conflicting types: ['PERSON', 'GPE']\n",
- "Type conflict detected: the Switch Phase conflicting types: ['FAC', 'ORG']\n",
- "Type conflict detected: 3′ conflicting types: ['CARDINAL', 'ORG']\n",
- "Type conflict detected: Chinmo conflicting types: ['ORG', 'PERSON']\n",
- "Type conflict detected: 15 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: CDKL5 conflicting types: ['PERSON', 'GPE']\n",
- "Type conflict detected: 50 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: BRCA1 conflicting types: ['CARDINAL', 'NORP', 'PERSON', 'GPE']\n",
- "Type conflict detected: Cezanne conflicting types: ['PERSON', 'GPE']\n",
- "Type conflict detected: HCAR2 conflicting types: ['ORG', 'GPE']\n",
- "Type conflict detected: HCAR3 conflicting types: ['ORG', 'GPE']\n",
- "Type conflict detected: 166 conflicting types: ['CARDINAL', 'MONEY']\n",
- "Type conflict detected: PrEP conflicting types: ['FAC', 'PERSON', 'ORG']\n",
- "Type conflict detected: 5 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: CI conflicting types: ['WORK_OF_ART', 'ORG']\n",
- "Type conflict detected: SAM conflicting types: ['WORK_OF_ART', 'ORG']\n",
- "Type conflict detected: 1.57 conflicting types: ['DATE', 'CARDINAL']\n",
- "Type conflict detected: Gavi conflicting types: ['PRODUCT', 'PERSON']\n",
- "Detected 19 conflict(s) in graph\n",
- "No conflicts were automatically resolved\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Graph: 2195 entities, 1848 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import GraphBuilder\n",
"\n",
@@ -780,22 +536,9 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Drugs similar to 'Aspirin':\n",
- "1. Hospital Episode Statistics (similarity: 0.810)\n",
- "2. SLE-DAS (similarity: 0.807)\n",
- "3. SLE-DAS (similarity: 0.807)\n",
- "4. AIS (similarity: 0.798)\n",
- "5. AIS (similarity: 0.798)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"query_drug = \"Aspirin\"\n",
"query_embedding = embedding_gen.generate_embeddings([query_drug])[0]\n",
@@ -820,24 +563,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Query: 'What drugs target COX enzymes?'\n",
- "Retrieved 2 results:\n",
- "\n",
- "1. Score: 0.806\n",
- "\n",
- "2. Score: 0.100\n",
- " Cox (PERSON).\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.context import AgentContext, ContextRetriever\n",
"\n",
@@ -895,25126 +623,9 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.plotly.v1+json": {
- "config": {
- "plotlyServerURL": "https://plot.ly"
- },
- "data": [
- {
- "hoverinfo": "none",
- "line": {
- "color": "#888",
- "width": 1
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- "mode": "lines",
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Type: PERSON",
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Type: PERSON",
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Type: DATE",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: PERSON",
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Type: GPE",
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Type: CARDINAL",
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Type: DATE",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: ORG",
- "31
Type: CARDINAL",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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- "Amy K. Ferketich
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: DATE",
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Type: PERCENT",
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Type: ORG",
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Type: ORG",
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Type: NORP",
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Type: DATE",
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Type: CARDINAL",
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Type: NORP",
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Type: CARDINAL",
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Type: ORG",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: DATE",
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Type: DATE",
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Type: DATE",
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Type: DATE",
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Type: PERCENT",
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Type: DATE",
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Type: PERCENT",
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Type: DATE",
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Type: DATE",
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Type: DATE",
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Type: PERCENT",
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Type: DATE",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: CARDINAL",
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Type: PRODUCT",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: PERSON",
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- "XY
Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Mujahid Ali Shah
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Frank Reimann
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: CARDINAL",
- "19
Type: CARDINAL",
- "23
Type: CARDINAL",
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Type: ORG",
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Type: CARDINAL",
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Type: ORG",
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Type: ORG",
- "EEC
Type: ORG",
- "Alexandros Gaitanidis
Type: PERSON",
- "Veronica Pampanin
Type: PERSON",
- "Jessica Thiem
Type: PERSON",
- "Kalaras
Type: PERSON",
- "Lion Huthmacher
Type: PERSON",
- "Silvan Hürkey
Type: PERSON",
- "Dimitrios Kadas
Type: PERSON",
- "Agapi Dimitriadou
Type: PERSON",
- "Andriana Ntogka
Type: PERSON",
- "Subhabrata Sanyal
Type: ORG",
- "Christos Consoulas
Type: PERSON",
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Type: ORG",
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Type: CARDINAL",
- "Yanying Yu
Type: PERSON",
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Type: PERSON",
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Type: ORG",
- "ML
Type: ORG",
- "AMR
Type: ORG",
- "ML
Type: ORG",
- "ML
Type: ORG",
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Type: ORG",
- "analyzing over 24,000
Type: CARDINAL",
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Type: ORG",
- "five
Type: CARDINAL",
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Type: ORG",
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Type: CARDINAL",
- "ML
Type: ORG",
- "AMR
Type: ORG",
- "Salsabil Arabi
Type: PERSON",
- "Chaoqun Ni
Type: PERSON",
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Type: PERSON",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: PERCENT",
- "Sofia Radrizzani
Type: PERSON",
- "Juan Rivas-Santisteban
Type: PERSON",
- "Namshik Han
Type: PERSON",
- "Laurence D. Hurst\n\n
Type: PERSON",
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Type: ORDINAL",
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Type: DATE",
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Type: ORDINAL",
- "~10
Type: ORG",
- "one
Type: CARDINAL",
- "first
Type: ORDINAL",
- "10
Type: CARDINAL",
- "three
Type: CARDINAL",
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Type: ORDINAL",
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Type: CARDINAL",
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Type: ORDINAL",
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Type: ORDINAL",
- "10
Type: CARDINAL",
- "5′
Type: GPE",
- "GC
Type: ORG",
- "Ya-Wen Hsueh
Type: PERSON",
- "Po-Min
Type: PERSON",
- "RNA
Type: ORG",
- "Split-Pool
Type: ORG",
- "PolydeoxyAdenylation
Type: ORG",
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Type: ORG",
- "3′
Type: ORG",
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Type: CARDINAL",
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Type: ORG",
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Type: ORG",
- "USPPAR
Type: ORG",
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Type: ORG",
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Type: GPE",
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Type: ORG",
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Type: CARDINAL",
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Type: ORG",
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Type: CARDINAL",
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Type: ORG",
- "Yuqing Hou
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Xi Cheng
Type: PERSON",
- "Bethany Walker
Type: PERSON",
- "Ying-Wai Lam
Type: PERSON",
- "Anna M. Schmoker
Type: PERSON",
- "Son N. Nguyen
Type: PERSON",
- "Maria Paz Gonzalez-Perez
Type: PERSON",
- "Bryan A. Ballif
Type: PERSON",
- "Karl F. Lechtreck
Type: PERSON",
- "George B. Witman
Type: PERSON",
- "Gregory J. Pazour\n\n
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: GPE",
- "LF5
Type: GPE",
- "CDKL5
Type: PERSON",
- "Mouse Cdkl5
Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: PERSON",
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Type: PERSON",
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Type: FAC",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: ORG",
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Type: PERSON",
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Type: ORG",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Ernst W. Schmid
Type: PERSON",
- "Ian J. Roney
Type: PERSON",
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Type: PERSON",
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Type: DATE",
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Type: CARDINAL",
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Type: CARDINAL",
- "22
Type: CARDINAL",
- "Knockout Sudoku
Type: PERSON",
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Type: ORG",
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Type: ORG",
- "Nicola K. Wilson\n\nRed
Type: PERSON",
- "PLOS Biology
Type: ORG",
- "Andrew C. Matthews
Type: PERSON",
- "Lehtinen
Type: PERSON",
- "Tatiana Dimitriu\n\n
Type: PERSON",
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Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "AMR
Type: ORG",
- "Kevin F. P. Bennett
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: LOC",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: GPE",
- "Yung Hwang
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Allon M. Klein\n\n
Type: PERSON",
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Type: PRODUCT",
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Type: PRODUCT",
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Type: PRODUCT",
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Type: PRODUCT",
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Type: PRODUCT",
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Type: PERSON",
- "Ádám Tiszlavicz
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: GPE",
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Type: GPE",
- "Gabor Tamas
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: NORP",
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Type: ORG",
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Type: ORG",
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Type: GPE",
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Type: ORG",
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Type: ORG",
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Type: ORG",
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Type: ORG",
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Type: ORG",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
- "Souradip Mukherjee
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Ricardo D. Righetto
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Jean Daraspe
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: ORG",
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Type: CARDINAL",
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Type: ORG",
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Type: ORG",
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Type: ORG",
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Type: ORG",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: DATE",
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Type: PERSON",
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Type: ORG",
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Type: PRODUCT",
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Type: PRODUCT",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: NORP",
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Type: ORG",
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Type: GPE",
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Type: ORG",
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Type: ORG",
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Type: PERSON",
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Type: ORG",
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Type: GPE",
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Type: PERSON",
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Type: CARDINAL",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: GPE",
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Type: ORG",
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Type: ORG",
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Type: GPE",
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Type: ORG",
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Type: ORG",
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Type: ORG",
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Type: CARDINAL",
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Type: PERCENT",
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Type: ORG",
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Type: ORG",
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Type: CARDINAL",
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Type: CARDINAL",
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Type: GPE",
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Type: NORP",
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Type: GPE",
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Type: ORG",
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Type: CARDINAL",
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Type: ORG",
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Type: GPE",
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Type: ORG",
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Type: ORG",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
- "Xinyu Wang
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Haiqing Tang
Type: PERSON",
- "Shanshan
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: PERSON",
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Type: ORG",
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Type: GPE",
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Type: GPE",
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Type: GPE",
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Type: NORP",
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Type: CARDINAL",
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Type: PERCENT",
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Type: PERCENT",
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Type: PERCENT",
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Type: GPE",
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Type: CARDINAL",
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Type: PERCENT",
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Type: PERCENT",
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Type: PERCENT",
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Type: PERCENT",
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Type: ORG",
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Type: GPE",
- "Rylee McGonigle
Type: PERSON",
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Type: PERSON",
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Type: PERSON",
- "Michelle Ouvina
Type: PERSON",
- "Alba Gutiérrez-Sacristán
Type: PERSON",
- "Anita K. Wagner
Type: PERSON",
- "Florence T. Bourgeois
Type: PERSON",
- "The U.S. Food and Drug Administration
Type: ORG",
- "FDA
Type: ORG",
- "the Pediatric Research Equity Act
Type: ORG",
- "PREA
Type: ORG",
- "FDA
Type: ORG",
- "2011
Type: DATE",
- "2023
Type: DATE",
- "at least one
Type: CARDINAL",
- "PREA
Type: ORG",
- "31 December 2024
Type: DATE",
- "FDA
Type: ORG",
- "552
Type: CARDINAL",
- "FDA
Type: ORG",
- "between 2011 and 2023
Type: DATE",
- "179
Type: CARDINAL",
- "32.4%
Type: PERCENT",
- "PREA
Type: ORG",
- "Thirteen
Type: CARDINAL",
- "166
Type: CARDINAL",
- "338
Type: CARDINAL",
- "About half
Type: CARDINAL",
- "51.8%
Type: PERCENT",
- "222
Type: CARDINAL",
- "31 December 2024
Type: DATE",
- "only 24.3%
Type: PERCENT",
- "Over half
Type: CARDINAL",
- "56.8%
Type: PERCENT",
- "an average of 2.9 years
Type: DATE",
- "2.0
Type: CARDINAL",
- "10 years
Type: DATE",
- "92.0%
Type: PERCENT",
- "59.5%
Type: PERCENT",
- "117
Type: CARDINAL",
- "31 December 2024
Type: DATE",
- "54.7%
Type: PERCENT",
- "64
Type: CARDINAL",
- "5.6 years
Type: DATE",
- "2.7
Type: CARDINAL",
- "8.3 years
Type: DATE",
- "3.3
Type: CARDINAL",
- "0.001
Type: CARDINAL",
- "90.4%
Type: PERCENT",
- "10 years
Type: DATE",
- "only 52.8%
Type: PERCENT",
- "PREA
Type: ORG",
- "FDA
Type: ORG",
- "just over half
Type: CARDINAL",
- "10 years
Type: DATE",
- "FDA
Type: ORG",
- "Fahmida Tofail
Type: PERSON",
- "Helen O. Pitchik
Type: PERSON",
- "Mahfuza Islam
Type: PERSON",
- "Rizwana Khan
Type: PERSON",
- "Abul K. Shoab
Type: PERSON",
- "Fahmida Akter
Type: PERSON",
- "Shirina Aktar
Type: PERSON",
- "Mahbubur Rahman
Type: PERSON",
- "Peter J. Winch
Type: PERSON",
- "Stephen P. Luby
Type: PERSON",
- "Lia C. H. Fernald\n
Type: PERSON",
- "Bangladesh
Type: GPE",
- "1 and 2 years of age
Type: DATE",
- "between May 31, 2012
Type: DATE",
- "July 7, 2013
Type: DATE",
- "WSH
Type: ORG",
- "WSH
Type: ORG",
- "5,551
Type: CARDINAL",
- "2-year
Type: DATE",
- "7-day
Type: DATE",
- "4,584
Type: CARDINAL",
- "5 years
Type: DATE",
- "7 years old
Type: DATE",
- "the Wechsler Pre and Primary Scale of Intelligence
Type: ORG",
- "linear
Type: ORG",
- "Between September 2019
Type: DATE",
- "February 2021
Type: DATE",
- "4,175
Type: CARDINAL",
- "720
Type: CARDINAL",
- "3,833
Type: CARDINAL",
- "718
Type: CARDINAL",
- "one
Type: CARDINAL",
- "0.10
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "0.00
Type: CARDINAL",
- "0.20
Type: CARDINAL",
- "0.15
Type: CARDINAL",
- "0.03
Type: CARDINAL",
- "the W, H, N
Type: ORG",
- "WSH
Type: ORG",
- "WSH
Type: ORG",
- "0.20
Type: CARDINAL",
- "0.07
Type: CARDINAL",
- "0.33
Type: CARDINAL",
- "0.31
Type: CARDINAL",
- "0.16
Type: CARDINAL",
- "0.46
Type: CARDINAL",
- "0.17
Type: CARDINAL",
- "0.01
Type: CARDINAL",
- "0.33
Type: CARDINAL",
- "0.40
Type: CARDINAL",
- "0.25
Type: CARDINAL",
- "FSIQ
Type: ORG",
- "6-month
Type: DATE",
- "COVID-19
Type: ORG",
- "7 years of age
Type: DATE",
- "NCT01590095.
Type: PERSON",
- "Danny Maupin
Type: PERSON",
- "Matt Spick
Type: PERSON",
- "Nophar Geifman\n\n
Type: PERSON",
- "Danny Maupin
Type: PERSON",
- "Matt Spick
Type: PERSON",
- "Nophar Geifman
Type: PERSON",
- "Angela Y. Chang
Type: PERSON",
- "Emily K. Johnson
Type: PERSON",
- "Sarah Bolongaita
Type: PERSON",
- "Kent Buse
Type: PERSON",
- "Sarah J. Hawkes
Type: PERSON",
- "Omar Karlsson
Type: PERSON",
- "Felicia M. Knaul
Type: PERSON",
- "Margaret E. Kruk
Type: PERSON",
- "Ole F. Norheim
Type: PERSON",
- "Ogbuoji
Type: PERSON",
- "David Watkins
Type: PERSON",
- "Dean T. Jamison\n
Type: PERSON",
- "First
Type: ORDINAL",
- "ages 0, 5
Type: DATE",
- "15
Type: DATE",
- "35
Type: DATE",
- "50
Type: DATE",
- "70
Type: DATE",
- "5th
Type: ORDINAL",
- "one
Type: CARDINAL",
- "1
Type: CARDINAL",
- "two
Type: CARDINAL",
- "between 13%
Type: PERCENT",
- "33%
Type: PERCENT",
- "age 70
Type: DATE",
- "237
Type: CARDINAL",
- "More than half
Type: CARDINAL",
- "India
Type: GPE",
- "approximately half
Type: CARDINAL",
- "the Middle East
Type: LOC",
- "North Africa
Type: GPE",
- "North Atlantic
Type: LOC",
- "Africa
Type: LOC",
- "Western Pacific
Type: LOC",
- "Southeast Asia
Type: LOC",
- "Africa
Type: LOC",
- "Western Pacific
Type: LOC",
- "Southeastern Asia
Type: LOC",
- "Eastern Europe
Type: LOC",
- "Sami L. Gottlieb
Type: PERSON",
- "Helen Rees
Type: PERSON",
- "syphilis
Type: GPE",
- "STI
Type: ORG",
- "syphilis
Type: GPE",
- "recent years
Type: DATE",
- "Sami Gottlieb
Type: PERSON",
- "Angela McLaughlin
Type: PERSON",
- "Junine Toy
Type: PERSON",
- "Vincent Montoya
Type: PERSON",
- "Paul Sereda
Type: PERSON",
- "Jason Trigg
Type: PERSON",
- "Mark Hull
Type: PERSON",
- "Chanson J. Brumme
Type: PERSON",
- "Rolando Barrios
Type: PERSON",
- "Julio S. G. Montaner
Type: PERSON",
- "Jeffrey B. Joy\n
Type: PERSON",
- "PrEP
Type: ORG",
- "PrEP
Type: FAC",
- "British Columbia
Type: GPE",
- "BC
Type: ORG",
- "Canada
Type: GPE",
- "January 2018
Type: DATE",
- "GBM
Type: ORG",
- "PrEP
Type: ORG",
- "PrEP
Type: PERSON",
- "BC
Type: ORG",
- "39
Type: CARDINAL",
- "566
Type: CARDINAL",
- "2018
Type: DATE",
- "2022
Type: DATE",
- "the BC Drug Treatment Program
Type: ORG",
- "at least one
Type: CARDINAL",
- "PrEP
Type: ORG",
- "0.0075
Type: CARDINAL",
- "carry baseline nucleoside analogue
Type: ORG",
- "0.025
Type: CARDINAL",
- "PrEP
Type: ORG",
- "BC
Type: ORG",
- "at least one
Type: CARDINAL",
- "2018
Type: DATE",
- "PrEP
Type: ORG",
- "GBM
Type: ORG",
- "PrEP
Type: ORG",
- "the end of 2017
Type: DATE",
- "Vancouver Coastal Health Authority
Type: ORG",
- "PrEP
Type: ORG",
- "BC
Type: ORG",
- "2018
Type: DATE",
- "2022
Type: DATE",
- "approximately 20
Type: CARDINAL",
- "PrEP
Type: ORG",
- "PrEP
Type: ORG",
- "PrEP
Type: PERSON",
- "Anuja Dokras
Type: PERSON",
- "Christos Coutifaris
Type: PERSON",
- "Alan T. Remaley
Type: PERSON",
- "Nehal N. Mehta
Type: PERSON",
- "Martin P. Playford
Type: PERSON",
- "Allen R. Kunselman
Type: PERSON",
- "Christy C. Stetter
Type: PERSON",
- "William C. Dodson
Type: PERSON",
- "Richard S. Legro
Type: PERSON",
- "and/or metformin
Type: PERSON",
- "first
Type: ORDINAL",
- "CVD
Type: ORG",
- "MetS
Type: ORG",
- "MetS
Type: ORG",
- "COMET-PCOS
Type: ORG",
- "≤40 years
Type: DATE",
- "BMI
Type: ORG",
- "48 kg
Type: QUANTITY",
- "Rotterdam
Type: PERSON",
- "1:1:1
Type: CARDINAL",
- "24 weeks
Type: DATE",
- "20
Type: CARDINAL",
- "estradiol/0.15
Type: PERSON",
- "metforminXR
Type: PERSON",
- "2,000
Type: CARDINAL",
- "MetS
Type: ORG",
- "MetS
Type: ORG",
- "MetS
Type: ORG",
- "WC
Type: ORG",
- "240
Type: CARDINAL",
- "20
Type: CARDINAL",
- "79
Type: CARDINAL",
- "16
Type: CARDINAL",
- "81
Type: CARDINAL",
- "17
Type: CARDINAL",
- "80
Type: CARDINAL",
- "169
Type: CARDINAL",
- "70.4%
Type: PERCENT",
- "between January 2018
Type: DATE",
- "June 2023
Type: DATE",
- "29.5 years
Type: DATE",
- "BMI
Type: ORG",
- "35.6 kg
Type: QUANTITY",
- "70%
Type: PERCENT",
- "White
Type: ORG",
- "23%
Type: PERCENT",
- "MetS
Type: ORG",
- "31%
Type: PERCENT",
- "MetS
Type: ORG",
- "26.2%
Type: PERCENT",
- "17/65
Type: CARDINAL",
- "28.6%
Type: PERCENT",
- "17/59
Type: CARDINAL",
- "Combined
Type: ORG",
- "28.8%
Type: PERCENT",
- "17/59
Type: CARDINAL",
- "MetS
Type: ORG",
- "0.26
Type: CARDINAL",
- "95%
Type: PERCENT",
- "0.01
Type: CARDINAL",
- "BMI
Type: ORG",
- "95%
Type: PERCENT",
- "0.01
Type: CARDINAL",
- "DXA
Type: ORG",
- "95%
Type: PERCENT",
- "0.001
Type: CARDINAL",
- "64%
Type: PERCENT",
- "24.1%
Type: PERCENT",
- "Gov Identifier: NCT03229057.
Type: PERSON",
- "Miguel Á. González-Gay
Type: PERSON",
- "Juan C. Quevedo-Abeledo
Type: PERSON",
- "Cristina Almeida-Santiago
Type: ORG",
- "Elena Heras-Recuero
Type: PERSON",
- "Antonia de Vera-González
Type: ORG",
- "Tejera-Segura
Type: PERSON",
- "Enrique García-Barrera
Type: PERSON",
- "Teresa Blázquez-Sánchez
Type: PERSON",
- "Luisa M. Villar
Type: PERSON",
- "Javier Gonzalo Ocejo-Vinyals
Type: PERSON",
- "Raquel Largo
Type: PERSON",
- "Iván Ferraz
Type: PERSON",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "400
Type: CARDINAL",
- "313
Type: CARDINAL",
- "SLE
Type: ORG",
- "September 2023 to February 2024
Type: DATE",
- "SLE-DAS
Type: ORG",
- "SLEDAI-2K
Type: ORG",
- "LLDAS
Type: ORG",
- "DORIS
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "Simoa (Single Molecule Array
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "Serum
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "Pearson
Type: PRODUCT",
- "0.369
Type: CARDINAL",
- "0.001
Type: CARDINAL",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "0.20
Type: CARDINAL",
- "95%
Type: PERCENT",
- "0.09
Type: CARDINAL",
- "0.32
Type: CARDINAL",
- "0.001
Type: CARDINAL",
- "SLE-DAS
Type: ORG",
- "0.15
Type: CARDINAL",
- "95%
Type: PERCENT",
- "0.05
Type: CARDINAL",
- "0.25
Type: CARDINAL",
- "0.003
Type: CARDINAL",
- "DORIS
Type: ORG",
- "LLDAS
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "> Conclusions
Type: ORG",
- "IFN
Type: ORG",
- "IFN
Type: ORG",
- "SLE
Type: ORG",
- "IFN
Type: ORG",
- "Li Chen
Type: PERSON",
- "Rongbin Xu
Type: PERSON",
- "Junqing Xie
Type: PERSON",
- "Yi Xing
Type: PERSON",
- "Bo Wen
Type: PERSON",
- "Yao Wu
Type: PERSON",
- "Binbin Su
Type: PERSON",
- "Mengjie Geng
Type: PERSON",
- "Xiang Ren
Type: PERSON",
- "Yi Zhang
Type: PERSON",
- "Jieyu Liu
Type: PERSON",
- "Xinli Song
Type: PERSON",
- "Yang Qin
Type: PERSON",
- "RuoLin Wang
Type: PERSON",
- "Jianuo Jiang
Type: PERSON",
- "Tongjun Guo
Type: PERSON",
- "Wen Yuan
Type: PERSON",
- "Yinghua Ma
Type: PERSON",
- "Yanhui Dong
Type: PERSON",
- "Yi Song
Type: PERSON",
- "Jun Ma
Type: PERSON",
- "Shanshan Li
Type: PERSON",
- "daily
Type: DATE",
- "4-24 years old
Type: DATE",
- "China Information System for Disease Control and Prevention
Type: ORG",
- "501
Type: CARDINAL",
- "2008
Type: DATE",
- "2019
Type: DATE",
- "0.25°×0.25°
Type: QUANTITY",
- "6,089,271
Type: CARDINAL",
- "1,034
Type: CARDINAL",
- "10
Type: CARDINAL",
- "5
Type: CARDINAL",
- "current day
Type: DATE",
- "28 days
Type: DATE",
- "6.8%
Type: PERCENT",
- "95%CI
Type: CARDINAL",
- "5.0%
Type: PERCENT",
- "8.7%
Type: PERCENT",
- "daily
Type: DATE",
- "1.2%
Type: PERCENT",
- "1.0%
Type: PERCENT",
- "1.4%
Type: PERCENT",
- "5
Type: CARDINAL",
- "28.6%
Type: PERCENT",
- "21.0%
Type: PERCENT",
- "36.8%
Type: PERCENT",
- "5.2%
Type: PERCENT",
- "2.3%
Type: PERCENT",
- "8.3%
Type: PERCENT",
- "12.6%
Type: PERCENT",
- "9.5%
Type: PERCENT",
- "15.8%
Type: PERCENT",
- "13.6%
Type: PERCENT",
- "5.6%
Type: PERCENT",
- "22.2%
Type: PERCENT",
- "only 2.7%
Type: PERCENT",
- "10.8%
Type: PERCENT",
- "annual
Type: DATE",
- "lower than 1.5
Type: CARDINAL",
- "29.7%
Type: PERCENT",
- "> Conclusions
Type: ORG",
- "Suzanne de Bruijn
Type: PERSON",
- "Alison Farrell
Type: PERSON",
- "Helen Lumbard
Type: PERSON",
- "Alexandra Tosun
Type: ORG",
- "Daniel Routledge\n\n
Type: PERSON",
- "recent years
Type: DATE",
- "Editorial
Type: PERSON",
- "recent years
Type: DATE",
- "Editorial
Type: PERSON",
- "Xiaojing Zeng
Type: PERSON",
- "Dongjian Yang
Type: PERSON",
- "Shiyang Li
Type: PERSON",
- "Xiaolin Hua
Type: PERSON",
- "Yanlin Wang
Type: PERSON",
- "Jun Zhang
Type: PERSON",
- "Zhiwei
Type: PERSON",
- "the Sustainable Development Goals
Type: ORG",
- "2030
Type: DATE",
- "the past two decades
Type: DATE",
- "China
Type: GPE",
- "China
Type: GPE",
- "2030
Type: DATE",
- "the National Health Statistics Yearbooks
Type: ORG",
- "China Statistical Yearbooks
Type: ORG",
- "2004
Type: DATE",
- "2013
Type: DATE",
- "the year
Type: DATE",
- "Reducing Maternal Mortality
Type: ORG",
- "Bayesian
Type: NORP",
- "years
Type: DATE",
- "East
Type: LOC",
- "2013
Type: DATE",
- "2013
Type: DATE",
- "East
Type: LOC",
- "West
Type: LOC",
- "2013
Type: DATE",
- "West
Type: LOC",
- "Eastern
Type: ORG",
- "Western China
Type: LOC",
- "2004-2020
Type: DATE",
- "Li-Tzu Wang
Type: PERSON",
- "Wu-Chien Chien
Type: PERSON",
- "Kevin Sheng-Kai Ma
Type: PERSON",
- "Chi-Hsiang Chung
Type: PERSON",
- "Yeu-Chin Chen
Type: PERSON",
- "Wei-Che Tsai
Type: PERSON",
- "Bing-Heng
Type: PERSON",
- "AHA
Type: ORG",
- "AHAs. Method
Type: ORG",
- "National Health Insurance Research Database
Type: ORG",
- "16-year
Type: DATE",
- "2000-2015
Type: DATE",
- "375,107
Type: CARDINAL",
- "AHA
Type: ORG",
- "two
Type: CARDINAL",
- "59,786
Type: CARDINAL",
- "180 days
Type: DATE",
- "239,144
Type: CARDINAL",
- "1:4
Type: CARDINAL",
- "52.19%
Type: PERCENT",
- "Kaplan-Meier
Type: ORG",
- "Cox
Type: PERSON",
- "16-year
Type: DATE",
- "hematologic neoplasms
Type: PERSON",
- "105.58
Type: CARDINAL",
- "100,000
Type: CARDINAL",
- "160.33
Type: CARDINAL",
- "0.789
Type: CARDINAL",
- "95%
Type: PERCENT",
- "0.667,0.913
Type: CARDINAL",
- ".001
Type: CARDINAL",
- "≥668 days
Type: DATE",
- "0.448
Type: CARDINAL",
- "95%
Type: PERCENT",
- "CI
Type: WORK_OF_ART",
- ".001
Type: CARDINAL",
- "0.552
Type: CARDINAL",
- "95%
Type: PERCENT",
- ".001
Type: CARDINAL",
- "0.555
Type: CARDINAL",
- "95%
Type: PERCENT",
- "0.457,0.689
Type: CARDINAL",
- ".001
Type: CARDINAL",
- "Daniel T. Myran
Type: PERSON",
- "Rachael MacDonald-Spracklin
Type: PERSON",
- "Michael Pugliese
Type: PERSON",
- "Maya Gibb
Type: PERSON",
- "Jess G. Fiedorowicz
Type: PERSON",
- "Tyler S. Kaster
Type: PERSON",
- "Marco
Type: PERSON",
- "BD
Type: ORG",
- "ED
Type: ORG",
- "14-65 years
Type: DATE",
- "BD
Type: ORG",
- "the Ontario Health Insurance Plan
Type: ORG",
- "Ontario
Type: GPE",
- "Canada
Type: GPE",
- "between 2008-2022
Type: DATE",
- "BD
Type: ORG",
- "Cox
Type: PERSON",
- "the past five years
Type: DATE",
- "9,311,844
Type: CARDINAL",
- "7,285
Type: CARDINAL",
- "0.08%
Type: PERCENT",
- "3-years
Type: DATE",
- "1.43%
Type: PERCENT",
- "104
Type: CARDINAL",
- "0.06%
Type: PERCENT",
- "41
Type: CARDINAL",
- "25
Type: CARDINAL",
- "6-fold
Type: CARDINAL",
- "Hazard Ratio
Type: PERSON",
- "5.97
Type: CARDINAL",
- "95%
Type: PERCENT",
- "CI 3.29
Type: DATE",
- "10.82
Type: CARDINAL",
- "BD
Type: ORG",
- "3.75
Type: CARDINAL",
- "2.49
Type: CARDINAL",
- "5.65
Type: CARDINAL",
- "2.50%
Type: PERCENT",
- "0.11%
Type: PERCENT",
- "Anthony S. Fauci
Type: PERSON",
- "Gregory K. Folkers\n\n
Type: PERSON",
- "World AIDS Day
Type: EVENT",
- "1988
Type: DATE",
- "2025
Type: DATE",
- "World AIDS Day 2025
Type: EVENT",
- "Gregory Folkers
Type: PERSON",
- "Anthony Fauci
Type: PERSON",
- "Zulfiqar A. Bhutta
Type: PERSON",
- "The Global Burden of Disease 2023
Type: WORK_OF_ART",
- "first
Type: ORDINAL",
- "1993
Type: DATE",
- "Editorial
Type: PERSON",
- "Zulfiqar Bhutta
Type: PERSON",
- "the Global Burden of Disease
Type: WORK_OF_ART",
- "GBD
Type: ORG",
- "2023
Type: DATE",
- "Jiaxin Xie
Type: PERSON",
- "Xuesi Dong
Type: PERSON",
- "Zilin Luo
Type: PERSON",
- "Chenran Wang
Type: PERSON",
- "Yadi Zheng
Type: PERSON",
- "Xiaolu Chen
Type: PERSON",
- "Zeming Guo
Type: PERSON",
- "Xiaoyue Shi
Type: PERSON",
- "Fei Wang
Type: PERSON",
- "Wei Cao
Type: PERSON",
- "Yongjie Xu
Type: PERSON",
- "Le Wang
Type: PERSON",
- "Weimiao Wu
Type: PERSON",
- "Dong Hang
Type: PERSON",
- "Lingbin Du
Type: PERSON",
- "Ni Li\n
Type: PERSON",
- "CRC
Type: ORG",
- "CRC
Type: ORG",
- "Chinese
Type: NORP",
- "Markov
Type: PERSON",
- "CRC-SIM
Type: ORG",
- "CRC
Type: ORG",
- "100,000
Type: CARDINAL",
- "40
Type: DATE",
- "79
Type: CARDINAL",
- "Two
Type: CARDINAL",
- "FIT
Type: ORG",
- "FIT
Type: ORG",
- "China National Medical Products Administration
Type: ORG",
- "one
Type: CARDINAL",
- "CRC
Type: ORG",
- "10%
Type: PERCENT",
- "CRC
Type: ORG",
- "FIT
Type: ORG",
- "224
Type: CARDINAL",
- "95%
Type: PERCENT",
- "CI
Type: ORG",
- "157
Type: CARDINAL",
- "292
Type: CARDINAL",
- "CRC
Type: ORG",
- "151
Type: CARDINAL",
- "95%
Type: PERCENT",
- "CI
Type: WORK_OF_ART",
- "109
Type: CARDINAL",
- "195
Type: CARDINAL",
- "NMPA
Type: ORG",
- "312
Type: CARDINAL",
- "95%
Type: PERCENT",
- "257
Type: CARDINAL",
- "360
Type: CARDINAL",
- "210
Type: CARDINAL",
- "95%
Type: PERCENT",
- "175
Type: CARDINAL",
- "241
Type: CARDINAL",
- "100%
Type: PERCENT",
- "10%
Type: PERCENT",
- "FIT
Type: ORG",
- "ICER
Type: ORG",
- "2,413
Type: MONEY",
- "QALY
Type: PERSON",
- "FIT
Type: ORG",
- "131.7
Type: MONEY",
- "at least 70%
Type: PERCENT",
- "50%
Type: PERCENT",
- "one
Type: CARDINAL",
- "CRC
Type: ORG",
- "FIT
Type: ORG",
- "China
Type: GPE",
- "CRC
Type: ORG",
- "Josh N. Lamb
Type: PERSON",
- "Adrian Sayers
Type: ORG",
- "Jeremy Mark Wilkinson
Type: PERSON",
- "Hemant Pandit
Type: ORG",
- "Michael R. Whitehouse\n
Type: PERSON",
- "THR
Type: ORG",
- "THR
Type: ORG",
- "POPFF
Type: ORG",
- "Reliance
Type: ORG",
- "THR
Type: ORG",
- "the National Joint Registry
Type: ORG",
- "Hospital Episode Statistics
Type: ORG",
- "01/01/2010
Type: ORG",
- "31/12/2020
Type: ORG",
- "first
Type: ORDINAL",
- "POPFF
Type: ORG",
- "372,967
Type: CARDINAL",
- "2,127,464
Type: CARDINAL",
- "years
Type: DATE",
- "5.39 years
Type: DATE",
- "0 to 12.1 years
Type: DATE",
- "8,043
Type: CARDINAL",
- "POPFF
Type: ORG",
- "3.78%
Type: PERCENT",
- "95%
Type: PERCENT",
- "CI
Type: ORG",
- "3.70%
Type: PERCENT",
- "3.86%
Type: PERCENT",
- "1,000
Type: CARDINAL",
- "3.00%
Type: PERCENT",
- "95%
Type: PERCENT",
- "CI
Type: ORG",
- "2.93%
Type: PERCENT",
- "3.07%
Type: PERCENT",
- "1,000
Type: CARDINAL",
- "10 years
Type: DATE",
- "3.1%
Type: PERCENT",
- "95%
Type: PERCENT",
- "CI
Type: ORG",
- "3.0%
Type: PERCENT",
- "3.1%
Type: PERCENT",
- "68 years
Type: DATE",
- "75 years
Type: DATE",
- "THR
Type: ORG",
- "Guy I. Sydney
Type: PERSON",
- "Kevan C. Herold
Type: PERSON",
- "1
Type: CARDINAL",
- "1
Type: CARDINAL",
- "1
Type: CARDINAL",
- "1
Type: CARDINAL",
- "Jehan N. Karim
Type: PERSON",
- "Jennifer M. Broughan
Type: PERSON",
- "Nicholas Aldridge
Type: PERSON",
- "Pranav Pandya
Type: PERSON",
- "Annette McHugh
Type: PERSON",
- "Aris T. Papageorghiou
Type: PERSON",
- "ACCEPTS
Type: ORG",
- "second
Type: ORDINAL",
- "around 20 weeks
Type: DATE",
- "first
Type: ORDINAL",
- "England
Type: GPE",
- "first
Type: ORDINAL",
- "first
Type: ORDINAL",
- "NHS
Type: ORG",
- "England
Type: GPE",
- "the National Congenital Anomaly and Rare Disease Registration Service
Type: ORG",
- "April 2017
Type: DATE",
- "March 2019
Type: DATE",
- "NHS
Type: ORG",
- "four
Type: CARDINAL",
- "14
Type: CARDINAL",
- "16 weeks
Type: DATE",
- "1,030,224
Type: CARDINAL",
- "110
Type: CARDINAL",
- "NHS
Type: ORG",
- "84%
Type: PERCENT",
- "5,895
Type: CARDINAL",
- "one
Type: CARDINAL",
- "First
Type: ORDINAL",
- "75%
Type: PERCENT",
- "32.7%
Type: PERCENT",
- "95%
Type: PERCENT",
- "31.5-33.9
Type: PRODUCT",
- "16 weeks
Type: DATE",
- "27.7%
Type: PERCENT",
- "95%
Type: PERCENT",
- "25.4-30.0
Type: DATE",
- "40.4%
Type: PERCENT",
- "95%
Type: PERCENT",
- "0.0001
Type: CARDINAL",
- "acrania
Type: GPE",
- "first
Type: ORDINAL",
- "first
Type: ORDINAL",
- "England
Type: GPE",
- "first
Type: ORDINAL",
- "Petra Heitkamp
Type: PERSON",
- "Obioma Chijioke-Akaniro
Type: PERSON",
- "Madhukar
Type: PERSON",
- "COVID-19
Type: ORG",
- "TB
Type: ORG",
- "Madhukar Pai
Type: PERSON",
- "TB
Type: ORG",
- "Epaminondas Markos Valsamis
Type: PERSON",
- "Josefine Beck Larsen
Type: PERSON",
- "Adrian Sayers
Type: ORG",
- "Timothy Jones
Type: PERSON",
- "Stephen E. Gwilym
Type: PERSON",
- "Pia Kjær-Kristensen
Type: PERSON",
- "Theis M. Thillemann
Type: PERSON",
- "Mechlenburg
Type: GPE",
- "Michael R. Whitehouse
Type: PERSON",
- "Jonathan L.
Type: PERSON",
- "BMI
Type: ORG",
- "BMI
Type: ORG",
- "18-100 years
Type: DATE",
- "the United Kingdom
Type: GPE",
- "Denmark
Type: GPE",
- "within 365 days
Type: DATE",
- "90 days
Type: DATE",
- "90 days
Type: DATE",
- "4.5 years
Type: DATE",
- "BMI
Type: ORG",
- "American Society of Anaesthesiologists
Type: ORG",
- "15,320
Type: CARDINAL",
- "5,446
Type: CARDINAL",
- "the United Kingdom
Type: GPE",
- "Denmark
Type: GPE",
- "the United Kingdom
Type: GPE",
- "72.2 years
Type: DATE",
- "68.3%
Type: PERCENT",
- "BMI
Type: ORG",
- "29.4 kg
Type: QUANTITY",
- "Denmark
Type: GPE",
- "70.5 years
Type: DATE",
- "65.3%
Type: PERCENT",
- "BMI
Type: ORG",
- "28.0 kg
Type: QUANTITY",
- "365-day
Type: DATE",
- "40 kg
Type: QUANTITY",
- "0.40
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "0.21
Type: CARDINAL",
- "0.73
Type: CARDINAL",
- "BMI
Type: ORG",
- "18.5 kg
Type: QUANTITY",
- "1.18
Type: CARDINAL",
- "1.06
Type: CARDINAL",
- "1.32
Type: CARDINAL",
- "21.75 kg
Type: QUANTITY",
- "90-day
Type: DATE",
- "1.69
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "1.14
Type: CARDINAL",
- "2.52
Type: CARDINAL",
- "90-day
Type: DATE",
- "1.36
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "1.05
Type: CARDINAL",
- "1.77
Type: CARDINAL",
- "1.70
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "1.25
Type: CARDINAL",
- "2.33
Type: CARDINAL",
- "BMI
Type: ORG",
- "BMI
Type: ORG",
- "131[UK
Type: CARDINAL",
- "365-day
Type: DATE",
- "obese
Type: NORP",
- "BMI
Type: ORG",
- "Clinicians
Type: NORP",
- "Jacques Wels
Type: PERSON",
- "ADE
Type: ORG",
- "Belgium
Type: GPE",
- "12
Type: CARDINAL",
- "the Federal Commission
Type: ORG",
- "2002
Type: DATE",
- "2023
Type: DATE",
- "33,592
Type: CARDINAL",
- "1.27%
Type: PERCENT",
- "0.92%
Type: PERCENT",
- "zero
Type: CARDINAL",
- "first
Type: ORDINAL",
- "three
Type: CARDINAL",
- "1.00
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "0.98
Type: CARDINAL",
- "1.03]-rate
Type: CARDINAL",
- "1.02
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "0.99
Type: CARDINAL",
- "1.04
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "1.00
Type: CARDINAL",
- "1.04
Type: CARDINAL",
- "95%CI
Type: CARDINAL",
- "Dutch
Type: NORP",
- "French
Type: LANGUAGE",
- "Belgium
Type: GPE",
- "between 2002
Type: DATE",
- "2023
Type: DATE",
- "Euthanasia
Type: ORG",
- "Laura Olbrich
Type: PERSON",
- "Larsson
Type: PERSON",
- "Rory Dunbar
Type: PERSON",
- "Peter J. Dodd
Type: PERSON",
- "Megan Palmer
Type: PERSON",
- "Anneke C. Hesseling
Type: PERSON",
- "Norbert Heinrich
Type: PERSON",
- "Heather J. Zar
Type: PERSON",
- "Nyanda E. Ntinginya
Type: PERSON",
- "Marriott Nliwasa
Type: PERSON",
- "Valsan P. Verghese
Type: PERSON",
- "Maryline Bonnet
Type: PERSON",
- "Eric Wobudeya
Type: PERSON",
- "Bwendo Nduna
Type: PERSON",
- "Raoul Moh
Type: PERSON",
- "Juliet Mwanga-Amumpere
Type: PERSON",
- "Ayeshatu Mustapha
Type: PERSON",
- "Jean-Voisin Taguebue
Type: PERSON",
- "Laurence Borand
Type: PERSON",
- "Chishala Chabala
Type: PERSON",
- "Olivier Marcy
Type: PERSON",
- "James A. Seddon
Type: PERSON",
- "Marieke M. van der Zalm
Type: PERSON",
- "the Decide-TB Study Group
Type: ORG",
- "RaPaed-TB Consortium
Type: ORG",
- "the Umoya Study Group
Type: ORG",
- "the TB Speed Consortium
Type: ORG",
- "2023
Type: DATE",
- "almost 200,000
Type: CARDINAL",
- "under 15 years
Type: DATE",
- "the World Health Organization (WHO
Type: ORG",
- "2022
Type: DATE",
- "IPD
Type: ORG",
- "four
Type: CARDINAL",
- "TB
Type: ORG",
- "10 years
Type: DATE",
- "SAM
Type: WORK_OF_ART",
- "2 years
Type: DATE",
- "IPD
Type: ORG",
- "TDA A
Type: ORG",
- "TDA B
Type: ORG",
- "1,886
Type: CARDINAL",
- "RaPaed-TB
Type: ORG",
- "740
Type: CARDINAL",
- "Umoya
Type: ORG",
- "474
Type: CARDINAL",
- "TB-Speed HIV
Type: ORG",
- "204
Type: CARDINAL",
- "TB-Speed Decentralisation
Type: ORG",
- "468
Type: CARDINAL",
- "2.9 years
Type: DATE",
- "741
Type: CARDINAL",
- "39.3%
Type: PERCENT",
- "2 years
Type: DATE",
- "382
Type: CARDINAL",
- "20.3%
Type: PERCENT",
- "CLHIV
Type: ORG",
- "284
Type: CARDINAL",
- "15.1%
Type: PERCENT",
- "SAM
Type: ORG",
- "281
Type: CARDINAL",
- "14.9%
Type: PERCENT",
- "672
Type: CARDINAL",
- "35.6%
Type: PERCENT",
- "933
Type: CARDINAL",
- "49.5%
Type: PERCENT",
- "84.3%
Type: PERCENT",
- "95%
Type: PERCENT",
- "74.8
Type: CARDINAL",
- "90.6
Type: CARDINAL",
- "90.6%
Type: PERCENT",
- "95%
Type: PERCENT",
- "83.8
Type: CARDINAL",
- "94.7
Type: CARDINAL",
- "50.6%
Type: PERCENT",
- "95%
Type: PERCENT",
- "30.4
Type: CARDINAL",
- "70.7
Type: CARDINAL",
- "30.8%
Type: PERCENT",
- "95%
Type: PERCENT",
- "21.5
Type: CARDINAL",
- "42.0
Type: CARDINAL",
- "TDA A
Type: ORG",
- "83.0%
Type: PERCENT",
- "95%
Type: PERCENT",
- "79.4%
Type: PERCENT",
- "86.1%
Type: PERCENT",
- "88.0%
Type: PERCENT",
- "95%
Type: PERCENT",
- "84.8%
Type: PERCENT",
- "90.6%
Type: PERCENT",
- "50.0%
Type: PERCENT",
- "95%
Type: PERCENT",
- "44.9%
Type: PERCENT",
- "55.1%
Type: PERCENT",
- "36.6%
Type: PERCENT",
- "95%
Type: PERCENT",
- "32.7%
Type: PERCENT",
- "40.7%
Type: PERCENT",
- "two
Type: CARDINAL",
- "IPD
Type: ORG",
- "tertiary
Type: ORDINAL",
- "IPD
Type: ORG",
- "IPD
Type: ORG",
- "TDA
Type: ORG",
- "Erfan Tasdighi
Type: PERSON",
- "Zhiqi Yao
Type: PERSON",
- "Zeina A. Dardari
Type: PERSON",
- "Kunal K. Jha
Type: PERSON",
- "Tanuja Rajan
Type: PERSON",
- "Ellen Boakye
Type: PERSON",
- "Kunihiro Matsushita
Type: PERSON",
- "Eleanor M. Simonsick
Type: PERSON",
- "Joao A. C. Lima
Type: PERSON",
- "Donald M. Lloyd-Jones
Type: PERSON",
- "Debbie L. Cohen
Type: PERSON",
- "Lawrence J. Appel
Type: PERSON",
- "Khera
Type: PERSON",
- "Michael E. Hall
Type: PERSON",
- "Carlos J. Rodriguez
Type: PERSON",
- "Suzanne Judd
Type: PERSON",
- "Shelley A. Cole
Type: PERSON",
- "Vasan S. Ramachandran
Type: PERSON",
- "Emelia
Type: GPE",
- "J. Benjamin
Type: PERSON",
- "Paulo A. Lotufo
Type: PERSON",
- "Marcio Sommer
Type: PERSON",
- "Samar R. El Khoudary
Type: PERSON",
- "Rebecca C. Thurston
Type: PERSON",
- "Carol A. Derby
Type: PERSON",
- "Bruce M. Psaty
Type: PERSON",
- "Charles B. Eaton
Type: PERSON",
- "Michael J. LaMonte
Type: PERSON",
- "Peggy M. Cawthon
Type: PERSON",
- "Eric S. Orwoll
Type: PERSON",
- "Aruni Bhatnagar
Type: PERSON",
- "Andrew P. DeFilippis
Type: PERSON",
- "Michael J. Blaha\n
Type: PERSON",
- "between pack-years
Type: DATE",
- "CPD
Type: ORG",
- "years
Type: DATE",
- "22
Type: CARDINAL",
- "the Cross-Cohort Collaboration Tobacco Working Group
Type: ORG",
- "CHD
Type: ORG",
- "CVD
Type: ORG",
- "AFib
Type: ORG",
- "CHD
Type: ORG",
- "CVD
Type: ORG",
- "14.4 years
Type: DATE",
- "MI
Type: ORG",
- "17,570
Type: CARDINAL",
- "19.3 years
Type: DATE",
- "CHD
Type: ORG",
- "30,625
Type: CARDINAL",
- "18.6 years
Type: DATE",
- "CVD
Type: ORG",
- "54,078
Type: CARDINAL",
- "approximately 19.4-19.9 years
Type: CARDINAL",
- "CHD
Type: ORG",
- "17,429
Type: CARDINAL",
- "CVD
Type: ORG",
- "33,120
Type: CARDINAL",
- "125,044
Type: CARDINAL",
- "323,826
Type: CARDINAL",
- "148,635
Type: CARDINAL",
- "176,396
Type: CARDINAL",
- "25
Type: CARDINAL",
- "16 million
Type: CARDINAL",
- "CVD
Type: ORG",
- "1.74
Type: CARDINAL",
- "95%
Type: PERCENT",
- "1.66,1.83
Type: CARDINAL",
- "2.07
Type: CARDINAL",
- "95%
Type: PERCENT",
- "2.00,2.14
Type: CARDINAL",
- "2.17
Type: CARDINAL",
- "95%
Type: PERCENT",
- "2.09,2.25
Type: CARDINAL",
- "2.43
Type: CARDINAL",
- "95%
Type: PERCENT",
- "2.38,2.48
Type: CARDINAL",
- "0.001
Type: CARDINAL",
- "2
Type: CARDINAL",
- "CPD
Type: ORG",
- "1.26
Type: CARDINAL",
- "95%
Type: PERCENT",
- "1.09,1.45
Type: CARDINAL",
- "0.002
Type: CARDINAL",
- "AFib
Type: ORG",
- "1.57
Type: CARDINAL",
- "95%
Type: PERCENT",
- "CI
Type: ORG",
- "1.39,1.78
Type: CARDINAL",
- "0.001
Type: CARDINAL",
- "2
Type: CARDINAL",
- "CPD
Type: ORG",
- "CVD
Type: ORG",
- "1.57
Type: DATE",
- "95%
Type: PERCENT",
- "1.41,1.75
Type: CARDINAL",
- "1.60
Type: CARDINAL",
- "95%
Type: PERCENT",
- "1.52,1.69
Type: CARDINAL",
- "0.001
Type: CARDINAL",
- "11
Type: CARDINAL",
- "CPD
Type: ORG",
- "CVD
Type: ORG",
- "1.87
Type: CARDINAL",
- "95%
Type: PERCENT",
- "1.69,2.06
Type: CARDINAL",
- "2.30
Type: CARDINAL",
- "95%
Type: PERCENT",
- "2.14,2.47
Type: CARDINAL",
- "0.001
Type: CARDINAL",
- "20
Type: CARDINAL",
- "20
Type: CARDINAL",
- "CPD
Type: ORG",
- "the first 10 years
Type: DATE",
- "80%
Type: PERCENT",
- "20 years
Type: DATE",
- "two
Type: CARDINAL",
- "Osondu Ogbuoji
Type: PERSON",
- "Ipchita Bharali
Type: PERSON",
- "Nonvignon
Type: PERSON",
- "Gavin Yamey\n\n
Type: PERSON",
- "Three
Type: CARDINAL",
- "Perspective
Type: ORG",
- "three
Type: CARDINAL",
- "Lisanne van Prooyen Schuurman
Type: PERSON",
- "Harry J. de Koning
Type: PERSON",
- "Eva Meier
Type: PERSON",
- "Robert-Jan H. Galjaard
Type: PERSON",
- "Nicolien T. van Ravesteyn\n
Type: PERSON",
- "first
Type: ORDINAL",
- "NIPT
Type: ORG",
- "first
Type: ORDINAL",
- "FCT
Type: ORG",
- "Dutch
Type: NORP",
- "second
Type: ORDINAL",
- "scan
Type: NORP",
- "175,000
Type: CARDINAL",
- "Dutch
Type: NORP",
- "21
Type: DATE",
- "18
Type: CARDINAL",
- "13
Type: CARDINAL",
- "Patau
Type: NORP",
- "FCT
Type: ORG",
- "166
Type: MONEY",
- "scan
Type: NORP",
- "191
Type: MONEY",
- "FCT
Type: ORG",
- "euploid fetal
Type: ORG",
- "GW-NIPT
Type: ORG",
- "545
Type: CARDINAL",
- "NIPT
Type: ORG",
- "514
Type: CARDINAL",
- "FCT
Type: ORG",
- "452
Type: CARDINAL",
- "152,785
Type: MONEY",
- "NIPT
Type: ORG",
- "159,852
Type: MONEY",
- "FCT
Type: ORG",
- "170,050
Type: MONEY",
- "GW-NIPT
Type: ORG",
- "NIPT
Type: ORG",
- "6
Type: CARDINAL",
- "FCT
Type: ORG",
- "13
Type: CARDINAL",
- "up to €467
Type: MONEY",
- "first
Type: ORDINAL",
- "GW-NIPT
Type: ORG",
- "years
Type: DATE",
- "Boshen Jiao
Type: PERSON",
- "Ryoko Sato
Type: PERSON",
- "Joshua Mak
Type: PERSON",
- "Bryan Patenaude
Type: PERSON",
- "Margaret de Villiers
Type: PERSON",
- "Aniruddha Deshpande
Type: PERSON",
- "Ivane Gamkrelidze
Type: PERSON",
- "Katy A. M. Gaythorpe
Type: PERSON",
- "Timothy B. Hallett
Type: PERSON",
- "Mark Jit
Type: PERSON",
- "Xiang Li
Type: PERSON",
- "Benjamin Lopman
Type: PERSON",
- "Shevanthi Nayagam
Type: PERSON",
- "Devin Razavi-Shearer
Type: PERSON",
- "Yvonne Tam
Type: PERSON",
- "Kim H. Woodruff
Type: PERSON",
- "Daniel Hogan
Type: PERSON",
- "Tewodaj Mengistu
Type: PERSON",
- "FRP
Type: ORG",
- "FRP
Type: ORG",
- "between 2000 and 2030
Type: DATE",
- "CHE
Type: ORG",
- "52
Type: CARDINAL",
- "Gavi
Type: PERSON",
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- "Patan Academy of Health Sciences",
- "Nepali",
- "Colaizzi",
- "Batchelor",
- "2017",
- "the Chronic Sorrow Instrument",
- "the PAHS Ethical Review Board",
- "Five",
- "Nepal",
- "Khalid Al Kubaisi",
- "Derar H. Abdel-Qader",
- "Nadia Al Mazrouei",
- "Abduelmula R. Abduelkarem",
- "Yahya H. Dallal",
- "Bashi Dallal Bashi",
- "Ahmad Shara\n",
- "Background",
- "up to 90%",
- "first",
- "Jordan",
- "first",
- "Jordanian",
- "380",
- "Two",
- "NVP",
- "NVP",
- "Appropriate Practice",
- "SPSS",
- "84.2%",
- "21.1%",
- "only 56.8%",
- "43.2%",
- "only 29.9%",
- "3.48",
- "2.01",
- "0.009",
- "50%",
- "0.50",
- "0.018).",
- "PLC",
- "PLC",
- "the Luoxizhou Super Bridge",
- "TOPSIS",
- "1",
- "2",
- "3",
- "TOPSIS",
- "Pareto",
- "4",
- "Jeanette K. Howard",
- "Analie R. Barnett",
- "Kurt A. Fesenmyer",
- "Mark G. Anderson",
- "TBI",
- "California",
- "RF",
- "TBI",
- "a century",
- "Yu-Ting Chi",
- "Naomi C. Hamm",
- "Hao Lee",
- "Minkyung Shin",
- "Yuki Liu",
- "Feibi Zheng",
- "Ben Forrest",
- "Peng-Lin",
- "MIS",
- "SureForm",
- "the Premier Healthcare Database",
- "PHD",
- "the United States",
- "(≥18 years",
- "bariatric",
- "2019 to 2023",
- "two",
- "SureForm",
- "bariatric",
- "Sophie Stanley",
- "Hilary Stewart",
- "UK",
- "three",
- "The Quality Evaluation Scoring Tool",
- "3394",
- "68",
- "25",
- "Two",
- "two",
- "firstly",
- "secondly",
- "Thea Lynne Hedemann",
- "Yun Lu",
- "Sofia Campitelli",
- "Lisa D. Hawke",
- "Nelson Shen",
- "Sarah Saperia",
- "Brett D. M. Jones",
- "Gillian Strudwick",
- "Chelsey R. Wilks",
- "Wei Wang",
- "Marco Solmi",
- "Michael Grossman",
- "Muhammad Ishrat",
- "Nicole Kozloff",
- "George Foussias",
- "Muhammad Omair",
- "CHR",
- "Dialectical",
- "DBT",
- "Digital",
- "CHR",
- "CHR",
- "two",
- "Phase 1",
- "DBT",
- "CHR",
- "Phase 2",
- "30",
- "30",
- "eight",
- "weekly",
- "DBT",
- "CHR",
- "Wafaa T. ALQadrie",
- "Ali M. Saleh",
- "Sami AL-Rawashdeh",
- "Ola N. Alfuqaha",
- "Suheir I. Abdallah\n",
- "recent years",
- "Jordan",
- "351",
- "Jordan",
- "Kelley Followership Questionnaire-Revised",
- "KFQ",
- "68.7%",
- "31.3%",
- "Chi square",
- "1",
- "6.193",
- "2",
- "7.405",
- ".025",
- "3",
- "13.926",
- ".003",
- "3",
- "17.173",
- ".001",
- "26",
- "67.77",
- ".001",
- "Cesare Garosi",
- "Cristina Vettori",
- "Roberta Ferrante",
- "Donatella",
- "13",
- "European",
- "J.F. Arnold",
- "ML",
- "Ks",
- "Ka",
- "ML",
- "616",
- "347",
- "269",
- "Ka/Ks",
- "13",
- "Ka/Ks",
- "Prabhnoor Tuli",
- "Abhay P. Kolte",
- "Rajashri A. Kolte",
- "Vrushali N. Lathiya",
- "Vinisha A. Bajaj",
- "Shahabe Saquib Abullais",
- "Manea M. Alahmari\n",
- "MAGRD",
- "VISTA",
- "RCT",
- "216",
- "MAGRD",
- "VISTA",
- "VISTA",
- "CRC",
- "12 months",
- "CAL",
- "GRD",
- "GRW",
- "6 months",
- "12",
- "12 months",
- "GRD",
- "GRW",
- "GT",
- "CRC",
- "95.92%",
- "85.02%).",
- "China",
- "2015 to 2023",
- "Nurin Yasmin",
- "Mohd Khairudin",
- "Awla Mohd Azraai",
- "Rosfaiizah Siran",
- "Nasibah",
- "estrogen",
- "Four",
- "Scopus",
- "Science Direct",
- "1",
- "January 1999",
- "31st October 2025",
- "549",
- "341",
- "293",
- "PRISMA",
- "31",
- "Amy Wermert",
- "Theodore M. Brasky",
- "Alison M. Newton",
- "Alice Hinton",
- "Hayley Curran",
- "Amy K. Ferketich",
- "Matthew J. Carpenter",
- "Peter G. Shields",
- "Patrick Tomko",
- "Theodore L. Wagener",
- "Brittney Keller-Hamilton\n",
- "Appalachia",
- "decades",
- "30%",
- "the Appalachian Research to Impact Smoking’s Effects",
- "NRT",
- "Appalachian",
- "Between 2025 and 2029",
- "1,000",
- "Appalachian",
- "11",
- "ONP",
- "NRT",
- "four",
- "Switch",
- "the Switch Phase",
- "7-day",
- "the Switch Phase",
- "César R. Peñaherrera-Palma",
- "Nicole S. Chinacalle-Martínez",
- "Wendy Gómez Zamora",
- "S. Pamela Jaramillo-Vallejo",
- "Jean P. López-Macías",
- "Omar Safady-Mendoza",
- "M. Estefanía Bravo",
- "Ecuador",
- "South America",
- "April to September 2023",
- "1,366",
- "22",
- "the decades of the 1980s, 1990s, 2000s",
- "2010s",
- "each fisher per decade",
- "approximately 99%",
- "the 2010s",
- "30%",
- "the pre-1980s",
- "approximately 59.5%",
- "the 2000s",
- "the decades",
- "60-100%",
- "the first 8",
- "Xiaoxin Guo",
- "Gang Du",
- "Juanyu Zhou",
- "Fang Fu",
- "Yu Yuan",
- "Xingzhu Liu",
- "Haiou Chen",
- "Qianyi Wan",
- "Bo Gong",
- "Haiyang",
- "ISC",
- "ISC",
- "PEX5",
- "RAB7",
- "ISC",
- "ISC",
- "VLCFA",
- "ISC",
- "Artyom",
- "PLOS Biology",
- "Drosophila",
- "Joanna Clarke",
- "2025",
- "the year",
- "2025",
- "Editorial",
- "the past year",
- "Dongwook Kim",
- "Byeongchan Kim",
- "Jinhu Kim",
- "Na-Young Seo",
- "Kim",
- "Kyung Ah Han",
- "Jubeen Yoon",
- "Christian P. Macks",
- "Joris de Wit",
- "Chang Ho Sohn",
- "Kea Joo Lee",
- "Ji Won Um",
- "Jaewon Ko",
- "Typhimurium",
- "Mg",
- "3′",
- "CRP",
- "Mg",
- "Mg",
- "Mg",
- "MgtC",
- "CRP",
- "Sneh Harsh",
- "Hsiao-Yun Liu",
- "Pradeep K. Bhaskar",
- "Christine Rushlow",
- "Erika A. Bach",
- "Chinmo",
- "Chinmo",
- "RNA",
- "Transformer",
- "transcription",
- "Zelda",
- "CySCs",
- "Chinmo",
- "Zld",
- "CySCs",
- "Two",
- "Qkr58E-2",
- "EcR",
- "CySCs",
- "EcR",
- "Zld",
- "Chinmo",
- "Zld",
- "XY",
- "Margaret Morash",
- "Richard G. Kay",
- "Erik J. Soderblom",
- "Grace H. MacLean",
- "Jia Wen",
- "Peyton J. Moore",
- "Colin R. Lickwar",
- "Mujahid Ali Shah",
- "Julia Ganz",
- "Fiona M. Gribble",
- "Frank Reimann",
- "Rodger A. Liddle",
- "John F. Rawls",
- "RNA",
- "six",
- "19",
- "23",
- "EEC",
- "EEC",
- "3",
- "EEC",
- "EEC",
- "EEC",
- "Alexandros Gaitanidis",
- "Veronica Pampanin",
- "Jessica Thiem",
- "Kalaras",
- "Lion Huthmacher",
- "Silvan Hürkey",
- "Dimitrios Kadas",
- "Agapi Dimitriadou",
- "Andriana Ntogka",
- "Subhabrata Sanyal",
- "Christos Consoulas",
- "Brain",
- "one",
- "Yanying Yu",
- "Nicole E. Wheeler",
- "AMR",
- "ML",
- "AMR",
- "ML",
- "ML",
- "AMR",
- "analyzing over 24,000",
- "AMR",
- "five",
- "ML",
- "6,740",
- "ML",
- "AMR",
- "Salsabil Arabi",
- "Chaoqun Ni",
- "B. Ian Hutchins",
- "approximately half",
- "15",
- "as little as 10%-20%",
- "Sofia Radrizzani",
- "Juan Rivas-Santisteban",
- "Namshik Han",
- "Laurence D. Hurst\n\n",
- "first",
- "less than around half",
- "first",
- "~10",
- "one",
- "first",
- "10",
- "three",
- "first",
- "two",
- "third",
- "first",
- "10",
- "5′",
- "GC",
- "Ya-Wen Hsueh",
- "Po-Min",
- "RNA",
- "Split-Pool",
- "PolydeoxyAdenylation",
- "USPPAR",
- "3′",
- "six",
- "PCR",
- "USPPAR",
- "USPPAR",
- "RNase",
- "Cu²⁺",
- "RNA",
- "one",
- "USPPAR",
- "five",
- "USPPAR",
- "Yuqing Hou",
- "Oranti Ahmed Omi",
- "Michael W. Stuck",
- "Xi Cheng",
- "Bethany Walker",
- "Ying-Wai Lam",
- "Anna M. Schmoker",
- "Son N. Nguyen",
- "Maria Paz Gonzalez-Perez",
- "Bryan A. Ballif",
- "Karl F. Lechtreck",
- "George B. Witman",
- "Gregory J. Pazour\n\n",
- "CDKL5",
- "CDKL5 Deficiency Disorder",
- "CDKL5",
- "LF5",
- "CDKL5",
- "Mouse Cdkl5",
- "CDKL5",
- "CDD",
- "Ben Vezina",
- "Helena B. Cooper",
- "Christopher K. Barlow",
- "Martin Rethoret-Pasty",
- "Sylvain Brisse",
- "Jonathan M. Monk",
- "Kathryn E. Holt",
- "Kelly L. Wyres",
- "Genomics",
- "hundreds",
- "7,835",
- "1,278",
- "this World Health Organization",
- "Ludivine Vasseur",
- "Céline Gheeraert",
- "Julie Dubois-Chevalier",
- "Ninon Very",
- "Loïc Guille",
- "Mohamed Bou Saleh",
- "Cyril Sobolewski",
- "Pascal Loyer",
- "Alexandre Berthier",
- "Noémie Legrand",
- "Anne Corlu",
- "Viviane Gnemmi",
- "Guillaume Lasailly",
- "Emmanuelle Leteurtre",
- "Dmitry Galinousky",
- "Antonino Bongiovanni",
- "Solenne Taront",
- "Nicolaj I. Toft",
- "Lars Grøntved",
- "David Tulasne",
- "Alessandro Furlan",
- "Line Carolle Ntandja-Wandji",
- "Bart Staels",
- "Philippe Lefebvre",
- "Laurent Dubuquoy",
- "Jérôme Eeckhoute",
- "HCT",
- "TF",
- "GRHL2",
- "HCT",
- "TF GRHL2",
- "Fernando H. Ramírez-Guadiana",
- "Anna P. Brogan",
- "Yuanchen Yu",
- "Caroline Midonet",
- "Joel W. Sher",
- "Ernst W. Schmid",
- "Ian J. Roney",
- "David Z. Rudner",
- "over half a century",
- "150",
- "50",
- "22",
- "Knockout Sudoku",
- "AlphaFold-Multimer",
- "IpdA",
- "Nicola K. Wilson\n\nRed",
- "PLOS Biology",
- "Andrew C. Matthews",
- "Lehtinen",
- "Tatiana Dimitriu\n\n",
- "AMR",
- "AMR",
- "AMR",
- "AMR",
- "AMR",
- "AMR",
- "AMR",
- "AMR",
- "AMR",
- "AMR",
- "AMR",
- "Kevin F. P. Bennett",
- "Andrew W. Wood",
- "Marcella D. Baiz",
- "Lan-Nhi Phung",
- "Irby J. Lovette",
- "David P. L. Toews\n\n",
- "0.5",
- "less than 100",
- "two",
- "one",
- "the Western Hemisphere",
- "Qiu C. Wu",
- "Aishwarya Swaminathan",
- "Ashley Winward",
- "Logan Lalonde",
- "Yung Hwang",
- "Noah Littman",
- "Merav Socolovsky",
- "Allon M. Klein\n\n",
- "IL-17",
- "IL-17",
- "IL-17",
- "IL-17",
- "IL-17",
- "Emoke Bakos",
- "Ádám Tiszlavicz",
- "Viktor Szegedi",
- "Abdennour Douida",
- "Furdan",
- "Daphne K. Welter",
- "Jonathan M. Landry",
- "Balazs Bende",
- "Gabor Hutoczki",
- "Pal Barzo",
- "Gabor Tamas",
- "Vladimir Benes",
- "Attila Szucs",
- "Karri Lamsa",
- "AP",
- "Kv1.2",
- "AIS",
- "AP",
- "Kv1",
- "AIS",
- "AIS",
- "AP",
- "AP",
- "AP",
- "AIS",
- "Eloïse Bertiaux",
- "Vincent Louvel",
- "Caitlyn L. McCafferty",
- "Hugo van",
- "Umut Batman",
- "Souradip Mukherjee",
- "Lorène Bournonville",
- "Olivier Mercey",
- "Ricardo D. Righetto",
- "Adrian Müller",
- "Philippe Van der Stappen",
- "Garrison Buss",
- "Jean Daraspe",
- "Christel Genoud",
- "Tim Stearns",
- "Benjamin D. Engel",
- "Virginie Hamel",
- "Paul Guichard",
- "Centrioles",
- "U-ExM",
- "U-ExM",
- "9-fold",
- "DISCO",
- "MNR/CEP90/OFD1",
- "DISCO",
- "MNR",
- "C2CD3/SFI1",
- "CEP162",
- "Heungjin Ryu",
- "Chie Hashimoto",
- "David A. Hill",
- "Keiko Mouri",
- "Keiko Shimizu",
- "daily",
- "Silke Stertz",
- "Umut Karakus",
- "MHC-II",
- "MHC-II",
- "Longqiang Wang",
- "Xiao Wu",
- "Atanu Paul",
- "Jun Yao",
- "Bin Wang",
- "BRCA1",
- "BRCA1",
- "BRCA1",
- "ubiquitin lysine 11",
- "Cezanne",
- "APC",
- "C E3",
- "Cezanne",
- "BRCA1 K11",
- "BRCA1",
- "BRCA1",
- "APC",
- "Cezanne",
- "BRCAness",
- "K11",
- "BRCA1",
- "BRCA1",
- "Alexandra",
- "Eudald Pascual-Carreras",
- "Kathrin Garschall",
- "Patrick R. H. Steinmetz\n\n",
- "TOR",
- "Vasa2+/Piwi1",
- "TOR",
- "TOR",
- "Vasa2+/Piwi1",
- "Fang Ye",
- "Zhiyi Zhang",
- "Binghao Zhang",
- "Xinyu Li",
- "Jiaxi Deng",
- "Qian Miao",
- "Peiruo Ning",
- "Yunlin Chi",
- "Geng Chen",
- "Zhangsong Wu",
- "Qian Wang",
- "Lezhi Xu",
- "Ningjie Gong",
- "Bangning Cheng",
- "Zhigang Ma",
- "Chungen Qian",
- "Lizhe Zhu",
- "Xin Pan",
- "Yang Du Methods",
- "South Africa",
- "African",
- "three",
- "13%",
- "7%",
- "31%",
- "South Africa",
- "2000−2020",
- "1,50%",
- "10,76%",
- "5%",
- "95%",
- "STI",
- "South Africa",
- "Rylee McGonigle",
- "Huseyin Naci",
- "Maximilian Siebert",
- "Michelle Ouvina",
- "Alba Gutiérrez-Sacristán",
- "Anita K. Wagner",
- "Florence T. Bourgeois",
- "The U.S. Food and Drug Administration",
- "FDA",
- "the Pediatric Research Equity Act",
- "PREA",
- "FDA",
- "2011",
- "2023",
- "at least one",
- "PREA",
- "31 December 2024",
- "FDA",
- "552",
- "FDA",
- "between 2011 and 2023",
- "179",
- "32.4%",
- "PREA",
- "Thirteen",
- "166",
- "338",
- "About half",
- "51.8%",
- "222",
- "31 December 2024",
- "only 24.3%",
- "Over half",
- "56.8%",
- "an average of 2.9 years",
- "2.0",
- "10 years",
- "92.0%",
- "59.5%",
- "117",
- "31 December 2024",
- "54.7%",
- "64",
- "5.6 years",
- "2.7",
- "8.3 years",
- "3.3",
- "0.001",
- "90.4%",
- "10 years",
- "only 52.8%",
- "PREA",
- "FDA",
- "just over half",
- "10 years",
- "FDA",
- "Fahmida Tofail",
- "Helen O. Pitchik",
- "Mahfuza Islam",
- "Rizwana Khan",
- "Abul K. Shoab",
- "Fahmida Akter",
- "Shirina Aktar",
- "Mahbubur Rahman",
- "Peter J. Winch",
- "Stephen P. Luby",
- "Lia C. H. Fernald\n",
- "Bangladesh",
- "1 and 2 years of age",
- "between May 31, 2012",
- "July 7, 2013",
- "WSH",
- "WSH",
- "5,551",
- "2-year",
- "7-day",
- "4,584",
- "5 years",
- "7 years old",
- "the Wechsler Pre and Primary Scale of Intelligence",
- "linear",
- "Between September 2019",
- "February 2021",
- "4,175",
- "720",
- "3,833",
- "718",
- "one",
- "0.10",
- "95%CI",
- "0.00",
- "0.20",
- "0.15",
- "0.03",
- "the W, H, N",
- "WSH",
- "WSH",
- "0.20",
- "0.07",
- "0.33",
- "0.31",
- "0.16",
- "0.46",
- "0.17",
- "0.01",
- "0.33",
- "0.40",
- "0.25",
- "FSIQ",
- "6-month",
- "COVID-19",
- "7 years of age",
- "NCT01590095.",
- "Danny Maupin",
- "Matt Spick",
- "Nophar Geifman\n\n",
- "Danny Maupin",
- "Matt Spick",
- "Nophar Geifman",
- "Angela Y. Chang",
- "Emily K. Johnson",
- "Sarah Bolongaita",
- "Kent Buse",
- "Sarah J. Hawkes",
- "Omar Karlsson",
- "Felicia M. Knaul",
- "Margaret E. Kruk",
- "Ole F. Norheim",
- "Ogbuoji",
- "David Watkins",
- "Dean T. Jamison\n",
- "First",
- "ages 0, 5",
- "15",
- "35",
- "50",
- "70",
- "5th",
- "one",
- "1",
- "two",
- "between 13%",
- "33%",
- "age 70",
- "237",
- "More than half",
- "India",
- "approximately half",
- "the Middle East",
- "North Africa",
- "North Atlantic",
- "Africa",
- "Western Pacific",
- "Southeast Asia",
- "Africa",
- "Western Pacific",
- "Southeastern Asia",
- "Eastern Europe",
- "Sami L. Gottlieb",
- "Helen Rees",
- "syphilis",
- "STI",
- "syphilis",
- "recent years",
- "Sami Gottlieb",
- "Angela McLaughlin",
- "Junine Toy",
- "Vincent Montoya",
- "Paul Sereda",
- "Jason Trigg",
- "Mark Hull",
- "Chanson J. Brumme",
- "Rolando Barrios",
- "Julio S. G. Montaner",
- "Jeffrey B. Joy\n",
- "PrEP",
- "PrEP",
- "British Columbia",
- "BC",
- "Canada",
- "January 2018",
- "GBM",
- "PrEP",
- "PrEP",
- "BC",
- "39",
- "566",
- "2018",
- "2022",
- "the BC Drug Treatment Program",
- "at least one",
- "PrEP",
- "0.0075",
- "carry baseline nucleoside analogue",
- "0.025",
- "PrEP",
- "BC",
- "at least one",
- "2018",
- "PrEP",
- "GBM",
- "PrEP",
- "the end of 2017",
- "Vancouver Coastal Health Authority",
- "PrEP",
- "BC",
- "2018",
- "2022",
- "approximately 20",
- "PrEP",
- "PrEP",
- "PrEP",
- "Anuja Dokras",
- "Christos Coutifaris",
- "Alan T. Remaley",
- "Nehal N. Mehta",
- "Martin P. Playford",
- "Allen R. Kunselman",
- "Christy C. Stetter",
- "William C. Dodson",
- "Richard S. Legro",
- "and/or metformin",
- "first",
- "CVD",
- "MetS",
- "MetS",
- "COMET-PCOS",
- "≤40 years",
- "BMI",
- "48 kg",
- "Rotterdam",
- "1:1:1",
- "24 weeks",
- "20",
- "estradiol/0.15",
- "metforminXR",
- "2,000",
- "MetS",
- "MetS",
- "MetS",
- "WC",
- "240",
- "20",
- "79",
- "16",
- "81",
- "17",
- "80",
- "169",
- "70.4%",
- "between January 2018",
- "June 2023",
- "29.5 years",
- "BMI",
- "35.6 kg",
- "70%",
- "White",
- "23%",
- "MetS",
- "31%",
- "MetS",
- "26.2%",
- "17/65",
- "28.6%",
- "17/59",
- "Combined",
- "28.8%",
- "17/59",
- "MetS",
- "0.26",
- "95%",
- "0.01",
- "BMI",
- "95%",
- "0.01",
- "DXA",
- "95%",
- "0.001",
- "64%",
- "24.1%",
- "Gov Identifier: NCT03229057.",
- "Miguel Á. González-Gay",
- "Juan C. Quevedo-Abeledo",
- "Cristina Almeida-Santiago",
- "Elena Heras-Recuero",
- "Antonia de Vera-González",
- "Tejera-Segura",
- "Enrique García-Barrera",
- "Teresa Blázquez-Sánchez",
- "Luisa M. Villar",
- "Javier Gonzalo Ocejo-Vinyals",
- "Raquel Largo",
- "Iván Ferraz",
- "IFN",
- "IFN",
- "IFN",
- "IFN",
- "IFN",
- "400",
- "313",
- "SLE",
- "September 2023 to February 2024",
- "SLE-DAS",
- "SLEDAI-2K",
- "LLDAS",
- "DORIS",
- "IFN",
- "IFN",
- "Simoa (Single Molecule Array",
- "IFN",
- "IFN",
- "IFN",
- "Serum",
- "IFN",
- "IFN",
- "Pearson",
- "0.369",
- "0.001",
- "IFN",
- "IFN",
- "0.20",
- "95%",
- "0.09",
- "0.32",
- "0.001",
- "SLE-DAS",
- "0.15",
- "95%",
- "0.05",
- "0.25",
- "0.003",
- "DORIS",
- "LLDAS",
- "IFN",
- "IFN",
- "> Conclusions",
- "IFN",
- "IFN",
- "SLE",
- "IFN",
- "Li Chen",
- "Rongbin Xu",
- "Junqing Xie",
- "Yi Xing",
- "Bo Wen",
- "Yao Wu",
- "Binbin Su",
- "Mengjie Geng",
- "Xiang Ren",
- "Yi Zhang",
- "Jieyu Liu",
- "Xinli Song",
- "Yang Qin",
- "RuoLin Wang",
- "Jianuo Jiang",
- "Tongjun Guo",
- "Wen Yuan",
- "Yinghua Ma",
- "Yanhui Dong",
- "Yi Song",
- "Jun Ma",
- "Shanshan Li",
- "daily",
- "4-24 years old",
- "China Information System for Disease Control and Prevention",
- "501",
- "2008",
- "2019",
- "0.25°×0.25°",
- "6,089,271",
- "1,034",
- "10",
- "5",
- "current day",
- "28 days",
- "6.8%",
- "95%CI",
- "5.0%",
- "8.7%",
- "daily",
- "1.2%",
- "1.0%",
- "1.4%",
- "5",
- "28.6%",
- "21.0%",
- "36.8%",
- "5.2%",
- "2.3%",
- "8.3%",
- "12.6%",
- "9.5%",
- "15.8%",
- "13.6%",
- "5.6%",
- "22.2%",
- "only 2.7%",
- "10.8%",
- "annual",
- "lower than 1.5",
- "29.7%",
- "> Conclusions",
- "Suzanne de Bruijn",
- "Alison Farrell",
- "Helen Lumbard",
- "Alexandra Tosun",
- "Daniel Routledge\n\n",
- "recent years",
- "Editorial",
- "recent years",
- "Editorial",
- "Xiaojing Zeng",
- "Dongjian Yang",
- "Shiyang Li",
- "Xiaolin Hua",
- "Yanlin Wang",
- "Jun Zhang",
- "Zhiwei",
- "the Sustainable Development Goals",
- "2030",
- "the past two decades",
- "China",
- "China",
- "2030",
- "the National Health Statistics Yearbooks",
- "China Statistical Yearbooks",
- "2004",
- "2013",
- "the year",
- "Reducing Maternal Mortality",
- "Bayesian",
- "years",
- "East",
- "2013",
- "2013",
- "East",
- "West",
- "2013",
- "West",
- "Eastern",
- "Western China",
- "2004-2020",
- "Li-Tzu Wang",
- "Wu-Chien Chien",
- "Kevin Sheng-Kai Ma",
- "Chi-Hsiang Chung",
- "Yeu-Chin Chen",
- "Wei-Che Tsai",
- "Bing-Heng",
- "AHA",
- "AHAs. Method",
- "National Health Insurance Research Database",
- "16-year",
- "2000-2015",
- "375,107",
- "AHA",
- "two",
- "59,786",
- "180 days",
- "239,144",
- "1:4",
- "52.19%",
- "Kaplan-Meier",
- "Cox",
- "16-year",
- "hematologic neoplasms",
- "105.58",
- "100,000",
- "160.33",
- "0.789",
- "95%",
- "0.667,0.913",
- ".001",
- "≥668 days",
- "0.448",
- "95%",
- "CI",
- ".001",
- "0.552",
- "95%",
- ".001",
- "0.555",
- "95%",
- "0.457,0.689",
- ".001",
- "Daniel T. Myran",
- "Rachael MacDonald-Spracklin",
- "Michael Pugliese",
- "Maya Gibb",
- "Jess G. Fiedorowicz",
- "Tyler S. Kaster",
- "Marco",
- "BD",
- "ED",
- "14-65 years",
- "BD",
- "the Ontario Health Insurance Plan",
- "Ontario",
- "Canada",
- "between 2008-2022",
- "BD",
- "Cox",
- "the past five years",
- "9,311,844",
- "7,285",
- "0.08%",
- "3-years",
- "1.43%",
- "104",
- "0.06%",
- "41",
- "25",
- "6-fold",
- "Hazard Ratio",
- "5.97",
- "95%",
- "CI 3.29",
- "10.82",
- "BD",
- "3.75",
- "2.49",
- "5.65",
- "2.50%",
- "0.11%",
- "Anthony S. Fauci",
- "Gregory K. Folkers\n\n",
- "World AIDS Day",
- "1988",
- "2025",
- "World AIDS Day 2025",
- "Gregory Folkers",
- "Anthony Fauci",
- "Zulfiqar A. Bhutta",
- "The Global Burden of Disease 2023",
- "first",
- "1993",
- "Editorial",
- "Zulfiqar Bhutta",
- "the Global Burden of Disease",
- "GBD",
- "2023",
- "Jiaxin Xie",
- "Xuesi Dong",
- "Zilin Luo",
- "Chenran Wang",
- "Yadi Zheng",
- "Xiaolu Chen",
- "Zeming Guo",
- "Xiaoyue Shi",
- "Fei Wang",
- "Wei Cao",
- "Yongjie Xu",
- "Le Wang",
- "Weimiao Wu",
- "Dong Hang",
- "Lingbin Du",
- "Ni Li\n",
- "CRC",
- "CRC",
- "Chinese",
- "Markov",
- "CRC-SIM",
- "CRC",
- "100,000",
- "40",
- "79",
- "Two",
- "FIT",
- "FIT",
- "China National Medical Products Administration",
- "one",
- "CRC",
- "10%",
- "CRC",
- "FIT",
- "224",
- "95%",
- "CI",
- "157",
- "292",
- "CRC",
- "151",
- "95%",
- "CI",
- "109",
- "195",
- "NMPA",
- "312",
- "95%",
- "257",
- "360",
- "210",
- "95%",
- "175",
- "241",
- "100%",
- "10%",
- "FIT",
- "ICER",
- "2,413",
- "QALY",
- "FIT",
- "131.7",
- "at least 70%",
- "50%",
- "one",
- "CRC",
- "FIT",
- "China",
- "CRC",
- "Josh N. Lamb",
- "Adrian Sayers",
- "Jeremy Mark Wilkinson",
- "Hemant Pandit",
- "Michael R. Whitehouse\n",
- "THR",
- "THR",
- "POPFF",
- "Reliance",
- "THR",
- "the National Joint Registry",
- "Hospital Episode Statistics",
- "01/01/2010",
- "31/12/2020",
- "first",
- "POPFF",
- "372,967",
- "2,127,464",
- "years",
- "5.39 years",
- "0 to 12.1 years",
- "8,043",
- "POPFF",
- "3.78%",
- "95%",
- "CI",
- "3.70%",
- "3.86%",
- "1,000",
- "3.00%",
- "95%",
- "CI",
- "2.93%",
- "3.07%",
- "1,000",
- "10 years",
- "3.1%",
- "95%",
- "CI",
- "3.0%",
- "3.1%",
- "68 years",
- "75 years",
- "THR",
- "Guy I. Sydney",
- "Kevan C. Herold",
- "1",
- "1",
- "1",
- "1",
- "Jehan N. Karim",
- "Jennifer M. Broughan",
- "Nicholas Aldridge",
- "Pranav Pandya",
- "Annette McHugh",
- "Aris T. Papageorghiou",
- "ACCEPTS",
- "second",
- "around 20 weeks",
- "first",
- "England",
- "first",
- "first",
- "NHS",
- "England",
- "the National Congenital Anomaly and Rare Disease Registration Service",
- "April 2017",
- "March 2019",
- "NHS",
- "four",
- "14",
- "16 weeks",
- "1,030,224",
- "110",
- "NHS",
- "84%",
- "5,895",
- "one",
- "First",
- "75%",
- "32.7%",
- "95%",
- "31.5-33.9",
- "16 weeks",
- "27.7%",
- "95%",
- "25.4-30.0",
- "40.4%",
- "95%",
- "0.0001",
- "acrania",
- "first",
- "first",
- "England",
- "first",
- "Petra Heitkamp",
- "Obioma Chijioke-Akaniro",
- "Madhukar",
- "COVID-19",
- "TB",
- "Madhukar Pai",
- "TB",
- "Epaminondas Markos Valsamis",
- "Josefine Beck Larsen",
- "Adrian Sayers",
- "Timothy Jones",
- "Stephen E. Gwilym",
- "Pia Kjær-Kristensen",
- "Theis M. Thillemann",
- "Mechlenburg",
- "Michael R. Whitehouse",
- "Jonathan L.",
- "BMI",
- "BMI",
- "18-100 years",
- "the United Kingdom",
- "Denmark",
- "within 365 days",
- "90 days",
- "90 days",
- "4.5 years",
- "BMI",
- "American Society of Anaesthesiologists",
- "15,320",
- "5,446",
- "the United Kingdom",
- "Denmark",
- "the United Kingdom",
- "72.2 years",
- "68.3%",
- "BMI",
- "29.4 kg",
- "Denmark",
- "70.5 years",
- "65.3%",
- "BMI",
- "28.0 kg",
- "365-day",
- "40 kg",
- "0.40",
- "95%CI",
- "0.21",
- "0.73",
- "BMI",
- "18.5 kg",
- "1.18",
- "1.06",
- "1.32",
- "21.75 kg",
- "90-day",
- "1.69",
- "95%CI",
- "1.14",
- "2.52",
- "90-day",
- "1.36",
- "95%CI",
- "1.05",
- "1.77",
- "1.70",
- "95%CI",
- "1.25",
- "2.33",
- "BMI",
- "BMI",
- "131[UK",
- "365-day",
- "obese",
- "BMI",
- "Clinicians",
- "Jacques Wels",
- "ADE",
- "Belgium",
- "12",
- "the Federal Commission",
- "2002",
- "2023",
- "33,592",
- "1.27%",
- "0.92%",
- "zero",
- "first",
- "three",
- "1.00",
- "95%CI",
- "0.98",
- "1.03]-rate",
- "1.02",
- "95%CI",
- "0.99",
- "1.04",
- "95%CI",
- "1.00",
- "1.04",
- "95%CI",
- "Dutch",
- "French",
- "Belgium",
- "between 2002",
- "2023",
- "Euthanasia",
- "Laura Olbrich",
- "Larsson",
- "Rory Dunbar",
- "Peter J. Dodd",
- "Megan Palmer",
- "Anneke C. Hesseling",
- "Norbert Heinrich",
- "Heather J. Zar",
- "Nyanda E. Ntinginya",
- "Marriott Nliwasa",
- "Valsan P. Verghese",
- "Maryline Bonnet",
- "Eric Wobudeya",
- "Bwendo Nduna",
- "Raoul Moh",
- "Juliet Mwanga-Amumpere",
- "Ayeshatu Mustapha",
- "Jean-Voisin Taguebue",
- "Laurence Borand",
- "Chishala Chabala",
- "Olivier Marcy",
- "James A. Seddon",
- "Marieke M. van der Zalm",
- "the Decide-TB Study Group",
- "RaPaed-TB Consortium",
- "the Umoya Study Group",
- "the TB Speed Consortium",
- "2023",
- "almost 200,000",
- "under 15 years",
- "the World Health Organization (WHO",
- "2022",
- "IPD",
- "four",
- "TB",
- "10 years",
- "SAM",
- "2 years",
- "IPD",
- "TDA A",
- "TDA B",
- "1,886",
- "RaPaed-TB",
- "740",
- "Umoya",
- "474",
- "TB-Speed HIV",
- "204",
- "TB-Speed Decentralisation",
- "468",
- "2.9 years",
- "741",
- "39.3%",
- "2 years",
- "382",
- "20.3%",
- "CLHIV",
- "284",
- "15.1%",
- "SAM",
- "281",
- "14.9%",
- "672",
- "35.6%",
- "933",
- "49.5%",
- "84.3%",
- "95%",
- "74.8",
- "90.6",
- "90.6%",
- "95%",
- "83.8",
- "94.7",
- "50.6%",
- "95%",
- "30.4",
- "70.7",
- "30.8%",
- "95%",
- "21.5",
- "42.0",
- "TDA A",
- "83.0%",
- "95%",
- "79.4%",
- "86.1%",
- "88.0%",
- "95%",
- "84.8%",
- "90.6%",
- "50.0%",
- "95%",
- "44.9%",
- "55.1%",
- "36.6%",
- "95%",
- "32.7%",
- "40.7%",
- "two",
- "IPD",
- "tertiary",
- "IPD",
- "IPD",
- "TDA",
- "Erfan Tasdighi",
- "Zhiqi Yao",
- "Zeina A. Dardari",
- "Kunal K. Jha",
- "Tanuja Rajan",
- "Ellen Boakye",
- "Kunihiro Matsushita",
- "Eleanor M. Simonsick",
- "Joao A. C. Lima",
- "Donald M. Lloyd-Jones",
- "Debbie L. Cohen",
- "Lawrence J. Appel",
- "Khera",
- "Michael E. Hall",
- "Carlos J. Rodriguez",
- "Suzanne Judd",
- "Shelley A. Cole",
- "Vasan S. Ramachandran",
- "Emelia",
- "J. Benjamin",
- "Paulo A. Lotufo",
- "Marcio Sommer",
- "Samar R. El Khoudary",
- "Rebecca C. Thurston",
- "Carol A. Derby",
- "Bruce M. Psaty",
- "Charles B. Eaton",
- "Michael J. LaMonte",
- "Peggy M. Cawthon",
- "Eric S. Orwoll",
- "Aruni Bhatnagar",
- "Andrew P. DeFilippis",
- "Michael J. Blaha\n",
- "between pack-years",
- "CPD",
- "years",
- "22",
- "the Cross-Cohort Collaboration Tobacco Working Group",
- "CHD",
- "CVD",
- "AFib",
- "CHD",
- "CVD",
- "14.4 years",
- "MI",
- "17,570",
- "19.3 years",
- "CHD",
- "30,625",
- "18.6 years",
- "CVD",
- "54,078",
- "approximately 19.4-19.9 years",
- "CHD",
- "17,429",
- "CVD",
- "33,120",
- "125,044",
- "323,826",
- "148,635",
- "176,396",
- "25",
- "16 million",
- "CVD",
- "1.74",
- "95%",
- "1.66,1.83",
- "2.07",
- "95%",
- "2.00,2.14",
- "2.17",
- "95%",
- "2.09,2.25",
- "2.43",
- "95%",
- "2.38,2.48",
- "0.001",
- "2",
- "CPD",
- "1.26",
- "95%",
- "1.09,1.45",
- "0.002",
- "AFib",
- "1.57",
- "95%",
- "CI",
- "1.39,1.78",
- "0.001",
- "2",
- "CPD",
- "CVD",
- "1.57",
- "95%",
- "1.41,1.75",
- "1.60",
- "95%",
- "1.52,1.69",
- "0.001",
- "11",
- "CPD",
- "CVD",
- "1.87",
- "95%",
- "1.69,2.06",
- "2.30",
- "95%",
- "2.14,2.47",
- "0.001",
- "20",
- "20",
- "CPD",
- "the first 10 years",
- "80%",
- "20 years",
- "two",
- "Osondu Ogbuoji",
- "Ipchita Bharali",
- "Nonvignon",
- "Gavin Yamey\n\n",
- "Three",
- "Perspective",
- "three",
- "Lisanne van Prooyen Schuurman",
- "Harry J. de Koning",
- "Eva Meier",
- "Robert-Jan H. Galjaard",
- "Nicolien T. van Ravesteyn\n",
- "first",
- "NIPT",
- "first",
- "FCT",
- "Dutch",
- "second",
- "scan",
- "175,000",
- "Dutch",
- "21",
- "18",
- "13",
- "Patau",
- "FCT",
- "166",
- "scan",
- "191",
- "FCT",
- "euploid fetal",
- "GW-NIPT",
- "545",
- "NIPT",
- "514",
- "FCT",
- "452",
- "152,785",
- "NIPT",
- "159,852",
- "FCT",
- "170,050",
- "GW-NIPT",
- "NIPT",
- "6",
- "FCT",
- "13",
- "up to €467",
- "first",
- "GW-NIPT",
- "years",
- "Boshen Jiao",
- "Ryoko Sato",
- "Joshua Mak",
- "Bryan Patenaude",
- "Margaret de Villiers",
- "Aniruddha Deshpande",
- "Ivane Gamkrelidze",
- "Katy A. M. Gaythorpe",
- "Timothy B. Hallett",
- "Mark Jit",
- "Xiang Li",
- "Benjamin Lopman",
- "Shevanthi Nayagam",
- "Devin Razavi-Shearer",
- "Yvonne Tam",
- "Kim H. Woodruff",
- "Daniel Hogan",
- "Tewodaj Mengistu",
- "FRP",
- "FRP",
- "between 2000 and 2030",
- "CHE",
- "52",
- "Gavi",
- "five",
- "CHE",
- "10%",
- "annual",
- "25% and 40%",
- "FRP",
- "CHE",
- "52",
- "Gavi",
- "2000",
- "2030",
- "about half",
- "CHE",
- "10%",
- "CHE",
- "first",
- "1,400",
- "CHE",
- "10,000",
- "44 million",
- "CHE",
- "FRP",
- "Naomi R. Waterlow",
- "Clare I. R. Chandler",
- "Ben S. Cooper",
- "Catrin E. Moore",
- "Julie V. Robotham",
- "Benn Sartorius",
- "Michael Sharland",
- "Gwenan M. Knight\n",
- ">Antimicrobial Resistance (",
- "AMR",
- "AMR",
- "AMR",
- "Europe",
- "United Nations",
- "UN",
- "12,807,473",
- "BSI",
- "Europe",
- "BSI",
- "8",
- "European Antimicrobial Resistance Surveillance Network",
- "2015-2019",
- "2022-2050",
- "BSI",
- "2022-2050",
- "Two",
- "Bayesian",
- "38",
- "2015-2019",
- "BSI",
- "year",
- "1,000",
- "BSI",
- "1",
- "5",
- "20",
- "100,000",
- "5",
- "100,000",
- "those older than 64 years",
- "Europe",
- "BSI",
- "6",
- "8",
- "74+ years",
- "2050",
- "0.34",
- "15.38",
- "2030).Not",
- "47%",
- "2030",
- "76%",
- "29/38",
- "11%",
- "10%",
- "BSI",
- "2030",
- "UN",
- "10%",
- "68.4%",
- "26/38",
- "BSI",
- "−20",
- "100,000",
- "10%",
- "BSI",
- "2050",
- "European",
- "BSI",
- "Europe",
- "2030",
- "AMR",
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- "ticks": ""
- },
- "bgcolor": "#E5ECF6",
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- "linecolor": "white",
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- }
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- "automargin": true,
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- "title": {
- "standoff": 15
- },
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- "zerolinewidth": 2
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- "title": {
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- },
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- }
- }
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- },
- "metadata": {},
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+ "outputs": [],
"source": [
"from semantica.visualization import KGVisualizer\n",
"\n",
@@ -26037,17 +648,9 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Exported knowledge graph to JSON and GraphML formats\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.export import GraphExporter\n",
"\n",
diff --git a/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb b/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb
index efa90df5..e284e6c7 100644
--- a/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb
+++ b/cookbook/use_cases/biomedical/02_Genomic_Variant_Analysis.ipynb
@@ -61,32 +61,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install -qU semantica networkx matplotlib plotly pandas groq sentence-transformers\n"
]
@@ -102,7 +79,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -113,7 +90,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -133,49 +110,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Ingesting from 12 feed sources...\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "🧠 Semantica - 📊 Current Progress
| Status | Action | Module | Submodule | File | Time |
|---|
| ✅ | Semantica is extracting | 🎯 semantic_extract | RelationExtractor | - | 0.68s |
| ✅ | Semantica is resolving | ⚠️ conflicts | ConflictDetector | - | 0.00s |
| ✅ | Semantica is resolving | ⚠️ conflicts | ConflictResolver | - | 0.00s |
| ✅ | Semantica is building | 🧠 kg | GraphBuilder | - | 0.01s |
| ✅ | Semantica is building | 🧠 kg | CentralityCalculator | - | 0.00s |
| ✅ | Semantica is building | 🧠 kg | CommunityDetector | - | 0.05s |
| ✅ | Semantica is reasoning | 🤔 reasoning | Reasoner | - | 0.01s |
| ✅ | Semantica is visualizing | 📈 visualization | KGVisualizer | - | 2.76s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | genomic_variant_kg.json | 0.08s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | genomic_variant_kg.graphml | 0.00s |
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " [1/12] PubMed - Genetics: Failed\n",
- " [2/12] PubMed - Genomics: Failed\n",
- " [3/12] PubMed - Variant Analysis: Failed\n",
- " [4/12] PubMed - GWAS: Failed\n",
- " [5/12] PubMed - Genomic Medicine: Failed\n",
- " [6/12] PubMed - Precision Medicine: Failed\n",
- " [7/12] PubMed - Pharmacogenomics: Failed\n",
- " [8/12] Nature Genetics: 30 documents\n",
- " [9/12] Nature - Genomics: 30 documents\n",
- " [10/12] PLOS Genetics: 30 documents\n",
- " [11/12] PLOS ONE - Genetics: 30 documents\n",
- " [12/12] Genome Research: Failed\n",
- "\n",
- "Total ingested: 121 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.ingest import FeedIngestor, FileIngestor\n",
"import os\n",
@@ -268,25 +205,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Normalizing 121 documents...\n",
- " Normalized 50/121 documents...\n",
- " Normalized 100/121 documents...\n",
- " Normalized 121/121 documents...\n",
- "Chunking 121 documents...\n",
- " Chunked 50/121 documents (50 chunks so far)\n",
- " Chunked 100/121 documents (133 chunks so far)\n",
- " Chunked 121/121 documents (177 chunks so far)\n",
- "Created 177 chunks from 121 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.normalize import TextNormalizer\n",
"from semantica.split import TextSplitter\n",
@@ -332,27 +253,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting entities from 177 chunks...\n",
- " Processed 20/177 chunks (17 entities found, 157 remaining)\n",
- " Processed 40/177 chunks (42 entities found, 137 remaining)\n",
- " Processed 60/177 chunks (65 entities found, 117 remaining)\n",
- " Processed 80/177 chunks (186 entities found, 97 remaining)\n",
- " Processed 100/177 chunks (351 entities found, 77 remaining)\n",
- " Processed 120/177 chunks (508 entities found, 57 remaining)\n",
- " Processed 140/177 chunks (667 entities found, 37 remaining)\n",
- " Processed 160/177 chunks (824 entities found, 17 remaining)\n",
- " Processed 177/177 chunks (985 entities found, 0 remaining)\n",
- "Extracted 6 variants, 250 genes, 237 diseases\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import NERExtractor\n",
"\n",
@@ -413,27 +316,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting relationships from 177 chunks...\n",
- " Processed 20/177 chunks (10 relationships found)\n",
- " Processed 40/177 chunks (29 relationships found)\n",
- " Processed 60/177 chunks (41 relationships found)\n",
- " Processed 80/177 chunks (180 relationships found)\n",
- " Processed 100/177 chunks (325 relationships found)\n",
- " Processed 120/177 chunks (449 relationships found)\n",
- " Processed 140/177 chunks (517 relationships found)\n",
- " Processed 160/177 chunks (655 relationships found)\n",
- " Processed 177/177 chunks (750 relationships found)\n",
- "Extracted 750 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import RelationExtractor\n",
"\n",
@@ -485,45 +370,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detecting conflicts in 985 entities, 750 relationships...\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Type conflict detected: T2 conflicting types: ['ORG', 'CARDINAL']\n",
- "Type conflict detected: Hop1 conflicting types: ['PERSON', 'GPE']\n",
- "Type conflict detected: NCR247 conflicting types: ['ORG', 'GPE']\n",
- "Type conflict detected: RNA conflicting types: ['ORG', 'PERSON']\n",
- "Type conflict detected: ASNA1 conflicting types: ['ORG', 'PERSON', 'GPE']\n",
- "Type conflict detected: ER conflicting types: ['ORG', 'GPE']\n",
- "Type conflict detected: ST160 conflicting types: ['PRODUCT', 'GPE']\n",
- "Type conflict detected: DREAM conflicting types: ['ORG', 'PERSON']\n",
- "Type conflict detected: Atf3 conflicting types: ['ORG', 'NORP']\n",
- "Type conflict detected: bariatric conflicting types: ['PERSON', 'GPE']\n",
- "Type conflict detected: the Switch Phase conflicting types: ['ORG', 'FAC']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detected 11 entity conflicts\n",
- "Detected 18 relationship conflicts\n",
- "Resolved 11 entity conflicts\n",
- "Resolved 18 relationship conflicts\n",
- "Conflicts resolved. GraphBuilder will use cleaned data.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.conflicts import ConflictDetector, ConflictResolver\n",
"\n",
@@ -587,23 +436,9 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building temporal knowledge graph from 985 entities, 750 relationships...\n",
- "Processing 985 entities, 750 relationships...\n",
- " Processed 500/985 entities (485 remaining)\n",
- " Processed 985/985 entities (complete)\n",
- " Processed 500/750 relationships (250 remaining)\n",
- " Processed 750/750 relationships (complete)\n",
- "Graph: 985 entities, 750 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import GraphBuilder\n",
"\n",
@@ -637,20 +472,9 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Graph analytics:\n",
- " - Communities: 4\n",
- " - Connected components: 1\n",
- " - Graph density: 0.000\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import GraphAnalyzer, CentralityCalculator, CommunityDetector\n",
"\n",
@@ -684,19 +508,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Temporal query: 750 relationships valid at query time\n",
- "Evolution analysis: 750 relationships tracked\n",
- "Temporal patterns detected: 0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import TemporalGraphQuery\n",
"\n",
@@ -737,19 +551,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Found 0 variants and 0 diseases\n",
- "Pathway analysis: 0 variant-disease pathways found\n",
- "Inferred facts: 0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.reasoning import Reasoner\n",
"from semantica.kg import GraphAnalyzer\n",
@@ -798,17 +602,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Top disease associations:\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import GraphAnalyzer\n",
"\n",
@@ -857,889 +653,9 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
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- "title": {
- "text": "Temporal Analysis Dashboard"
- },
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- "xaxis": {
- "anchor": "y",
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- 0,
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- ]
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- }
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"from semantica.visualization import TemporalVisualizer\n",
"\n",
@@ -1765,17 +681,9 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Exported knowledge graph to JSON and GraphML formats\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.export import GraphExporter\n",
"\n",
diff --git a/cookbook/use_cases/blockchain/01_DeFi_Protocol_Intelligence.ipynb b/cookbook/use_cases/blockchain/01_DeFi_Protocol_Intelligence.ipynb
index dcac8868..a71599de 100644
--- a/cookbook/use_cases/blockchain/01_DeFi_Protocol_Intelligence.ipynb
+++ b/cookbook/use_cases/blockchain/01_DeFi_Protocol_Intelligence.ipynb
@@ -59,32 +59,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq sentence-transformers scikit-learn\n"
]
@@ -98,7 +75,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -122,42 +99,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Ingesting from 6 feed sources...\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "🧠 Semantica - 📊 Current Progress
| Status | Action | Module | Submodule | File | Time |
|---|
| ✅ | Semantica is processing | 🔗 context | ContextRetriever | - | 27.02s |
| ❌ | Semantica is embedding | 💾 embeddings | TextEmbedder | - | 0.00s |
| ✅ | Semantica is processing | 🔗 context | AgentMemory | - | 0.04s |
| ✅ | Semantica is resolving | ⚠️ conflicts | ConflictDetector | - | 0.13s |
| ✅ | Semantica is resolving | ⚠️ conflicts | ConflictResolver | - | 0.00s |
| ✅ | Semantica is visualizing | 📈 visualization | KGVisualizer | - | 14.90s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | defi_protocol_kg.json | 0.06s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | defi_protocol_kg.graphml | 0.00s |
| ❌ | Semantica is exporting | 💾 export | GraphExporter | defi_ontology.ttl | 0.00s |
| ✅ | Semantica is exporting | 💾 export | RDFExporter | - | 0.03s |
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " [1/6] CoinDesk: 25 documents\n",
- " [2/6] CoinTelegraph: 30 documents\n",
- " [3/6] Decrypt: 51 documents\n",
- " [4/6] The Block: 19 documents\n",
- " [5/6] CryptoSlate: 10 documents\n",
- " [6/6] CryptoNews: 20 documents\n",
- "Ingested 155 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.ingest import FeedIngestor, FileIngestor, WebIngestor\n",
"import os\n",
@@ -223,21 +167,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Parsing 155 documents...\n",
- " Parsed 50/155 documents...\n",
- " Parsed 100/155 documents...\n",
- " Parsed 150/155 documents...\n",
- " Parsed 155/155 documents...\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.parse import DocumentParser\n",
"\n",
@@ -269,27 +201,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Normalizing 155 documents...\n",
- " Normalized 50/155 documents...\n",
- " Normalized 100/155 documents...\n",
- " Normalized 150/155 documents...\n",
- " Normalized 155/155 documents...\n",
- "Chunking 155 documents...\n",
- " Chunked 50/155 documents (50 chunks so far)\n",
- " Chunked 100/155 documents (101 chunks so far)\n",
- " Chunked 150/155 documents (151 chunks so far)\n",
- " Chunked 155/155 documents (156 chunks so far)\n",
- "Created 156 chunks from 155 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.normalize import TextNormalizer\n",
"from semantica.split import TextSplitter\n",
@@ -342,24 +256,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting entities from 156 chunks using ML (spaCy)...\n",
- "\n",
- "✅ Extraction complete!\n",
- " Total entities: 2406\n",
- " Standard labels: ['CARDINAL', 'GPE', 'ORG', 'PERSON', 'FAC', 'MONEY', 'DATE', 'NORP', 'LOC', 'ORDINAL', 'PRODUCT', 'PERCENT', 'WORK_OF_ART', 'EVENT', 'TIME', 'LAW']\n",
- " Protocols (filtered): 631\n",
- " Tokens (filtered): 290\n",
- " Risks (filtered): 0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import NERExtractor\n",
"\n",
@@ -418,26 +317,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting relationships from 156 chunks...\n",
- " Processed 20/156 chunks (36 relationships found)\n",
- " Processed 40/156 chunks (105 relationships found)\n",
- " Processed 60/156 chunks (168 relationships found)\n",
- " Processed 80/156 chunks (214 relationships found)\n",
- " Processed 100/156 chunks (359 relationships found)\n",
- " Processed 120/156 chunks (431 relationships found)\n",
- " Processed 140/156 chunks (567 relationships found)\n",
- " Processed 156/156 chunks (638 relationships found)\n",
- "Extracted 638 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import RelationExtractor\n",
"\n",
@@ -495,79 +377,9 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detecting entity conflicts in 2406 entities...\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Value conflict detected: AI.label has conflicting values: ['GPE', 'ORG']\n",
- "Value conflict detected: 124.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: Clear Street's.label has conflicting values: ['FAC', 'ORG']\n",
- "Value conflict detected: more than 125 million.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: start_char=31.label has conflicting values: ['PERSON', 'GPE']\n",
- "Value conflict detected: Binance.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: 100.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: BlackRock.label has conflicting values: ['GPE', 'ORG']\n",
- "Value conflict detected: 143.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: Circle.label has conflicting values: ['LOC', 'NORP']\n",
- "Value conflict detected: Bitcoin.label has conflicting values: ['PERSON', 'NORP', 'ORG']\n",
- "Value conflict detected: 128.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: Coinbase.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: start_char=78.label has conflicting values: ['PERSON', 'GPE']\n",
- "Value conflict detected: Trump.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: 133.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: Ethereum.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: Kontigo.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: 2025.label has conflicting values: ['DATE', 'CARDINAL']\n",
- "Value conflict detected: Fundstrat.label has conflicting values: ['PERSON', 'NORP']\n",
- "Value conflict detected: BTC.label has conflicting values: ['NORP', 'ORG']\n",
- "Value conflict detected: Solana.label has conflicting values: ['PERSON', 'GPE']\n",
- "Value conflict detected: BCH.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: Lugano.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: Bitcoiner.label has conflicting values: ['PERSON', 'NORP']\n",
- "Value conflict detected: 20.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: end_char=76.label has conflicting values: ['DATE', 'CARDINAL']\n",
- "Value conflict detected: Dogecoin.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: 136.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: start_char=115.label has conflicting values: ['GPE', 'ORG']\n",
- "Value conflict detected: 200,000.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: 2,930.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: 186.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: Netflix.label has conflicting values: ['GPE', 'ORG']\n",
- "Value conflict detected: Tether.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: 142.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: 2,820.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: RLUSD.label has conflicting values: ['PERSON', 'ORG']\n",
- "Value conflict detected: 50.label has conflicting values: ['MONEY', 'CARDINAL']\n",
- "Value conflict detected: Magic.label has conflicting values: ['PRODUCT', 'ORG']\n",
- "Value conflict detected: 2025: Failed to establish a new connection: [WinError 10061] No connection could be made because the target machine actively refused it'))\n",
- "Batch 0 failed, retrying: Not connected to Blazegraph\n",
- "Batch 0 failed, retrying: Not connected to Blazegraph\n",
- "Batch 0 failed after 3 attempts: Not connected to Blazegraph\n",
- "Batch 1 failed, retrying: Not connected to Blazegraph\n",
- "Batch 1 failed, retrying: Not connected to Blazegraph\n",
- "Batch 1 failed after 3 attempts: Not connected to Blazegraph\n",
- "Batch 2 failed, retrying: Not connected to Blazegraph\n",
- "Batch 2 failed, retrying: Not connected to Blazegraph\n",
- "Batch 2 failed after 3 attempts: Not connected to Blazegraph\n",
- "Batch 3 failed, retrying: Not connected to Blazegraph\n",
- "Batch 3 failed, retrying: Not connected to Blazegraph\n",
- "Batch 3 failed after 3 attempts: Not connected to Blazegraph\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "⚠ Stored 0/3770 triplets (3770 failed)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.triplet_store import TripletStore\n",
"\n",
@@ -970,38 +690,9 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Embedding generation failed: Text cannot be empty or whitespace-only\n",
- "Using random fallback embedding\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "GraphRAG query: 'What protocols have high risk?'\n",
- "\n",
- "Retrieved 3 results:\n",
- "\n",
- "1. Score: 0.594\n",
- " Content: Bitcoin Mining is a PERSON. is payed by ASIC (ORG). be confidence=1.0 (ORG)....\n",
- " Related entities: 2\n",
- "\n",
- "2. Score: 0.500\n",
- " Content: ...\n",
- "\n",
- "3. Score: 0.228\n",
- " Content: 2026 is a DATE....\n",
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.context import AgentContext\n",
"\n",
@@ -1036,19239 +727,9 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "📊 Knowledge Graph Statistics:\n",
- " Entities: 2406\n",
- " Relationships: 638\n",
- " Density: 0.0002\n",
- "\n"
- ]
- },
- {
- "data": {
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- "xaxis": {
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- "metadata": {},
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- "output_type": "stream",
- "text": [
- "\n",
- "✅ Interactive visualization displayed!\n",
- " Use the toolbar above to zoom, pan, and interact with the graph\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.visualization import KGVisualizer\n",
"import plotly.graph_objects as go\n",
@@ -20425,18 +886,9 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Exported knowledge graph to JSON and GraphML formats\n",
- "✅ Exported ontology to RDF/TTL format\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.export import GraphExporter, RDFExporter\n",
"\n",
diff --git a/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb b/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb
index 84e4e6dc..4a1cb7d6 100644
--- a/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb
+++ b/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb
@@ -1,18029 +1,926 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "[](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb)\n",
- "\n",
- "# Transaction Network Analysis - Pattern Detection & Graph Analytics\n",
- "\n",
- "## Overview\n",
- "\n",
- "This notebook demonstrates **blockchain transaction network analysis** using Semantica with focus on **pattern detection**, **network analytics**, and **real-time processing**. The pipeline analyzes blockchain transaction networks to detect patterns, identify whale movements, and analyze token flows.\n",
- "\n",
- "### Key Features\n",
- "\n",
- "- **Pattern Detection**: Emphasizes graph analytics for transaction pattern recognition\n",
- "- **Network Analytics**: Uses centrality measures and community detection\n",
- "- **Temporal Analysis**: Time-aware queries and transaction evolution tracking\n",
- "- **Whale Tracking**: Identifies large transaction movements\n",
- "- **Flow Analysis**: Analyzes token flows through the network\n",
- "- **Comprehensive Data Sources**: Multiple blockchain APIs, analytics platforms, and databases\n",
- "\n",
- "### Learning Objectives\n",
- "\n",
- "- Ingest blockchain transaction data from multiple sources\n",
- "- Extract transaction entities (Transactions, Wallets, Addresses, Blocks, Flows)\n",
- "- Build temporal transaction network graphs\n",
- "- Perform graph analytics (centrality, communities, connectivity)\n",
- "- Detect patterns and whale movements\n",
- "- Analyze token flows and transaction paths\n",
- "- Store and query transaction data using vector stores and graph stores\n",
- "\n",
- "### Pipeline Flow\n",
- "\n",
- "```mermaid\n",
- "graph TD\n",
- " A[Data Ingestion] --> B[Document Parsing]\n",
- " B --> C[Text Processing]\n",
- " C --> D[Entity Extraction]\n",
- " D --> E[Relationship Extraction]\n",
- " E --> F[Deduplication]\n",
- " F --> G[Conflict Detection]\n",
- " G --> H[Transaction Network Graph]\n",
- " H --> I[Embeddings]\n",
- " I --> J[Vector Store]\n",
- " H --> K[Graph Analytics]\n",
- " K --> L[Temporal Queries]\n",
- " L --> M[Pattern Detection]\n",
- " M --> N[Flow Analysis]\n",
- " J --> O[GraphRAG Queries]\n",
- " H --> P[Graph Store]\n",
- " O --> Q[Visualization]\n",
- " P --> Q\n",
- " Q --> R[Export]\n",
- "```\n",
- "\n",
- "## Installation\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
- ]
- }
- ],
- "source": [
- "%pip install -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq sentence-transformers scikit-learn\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Configuration & Setup\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "\n",
- "os.environ[\"GROQ_API_KEY\"] = os.getenv(\"GROQ_API_KEY\", \"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\")\n",
- "\n",
- "# Configuration constants\n",
- "EMBEDDING_DIMENSION = 384\n",
- "EMBEDDING_MODEL = \"all-MiniLM-L6-v2\"\n",
- "CHUNK_SIZE = 1000\n",
- "CHUNK_OVERLAP = 200\n",
- "TEMPORAL_GRANULARITY = \"day\"\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Ingesting Blockchain Transaction Data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Ingesting from 8 RSS feed sources...\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "🧠 Semantica - 📊 Current Progress
| Status | Action | Module | Submodule | File | Time |
|---|
| ✅ | Semantica is building | 🧠 kg | CommunityDetector | - | 0.03s |
| ✅ | Semantica is processing | 🔗 context | ContextRetriever | - | 25.99s |
| ❌ | Semantica is embedding | 💾 embeddings | TextEmbedder | - | 0.00s |
| ✅ | Semantica is processing | 🔗 context | AgentMemory | - | 0.03s |
| ✅ | Semantica is visualizing | 📈 visualization | KGVisualizer | - | 5.78s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | transaction_network.json | 0.09s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | transaction_network.graphml | 0.01s |
| ❌ | Semantica is exporting | 💾 export | GraphExporter | transaction_network.csv | 0.01s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | transaction_network.gexf | 0.01s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | transaction_network.dot | 0.01s |
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " [1/8] CoinDesk: 25 documents\n",
- " [2/8] CoinTelegraph: 30 documents\n",
- " [3/8] Decrypt: 52 documents\n",
- " [4/8] The Block: 19 documents\n",
- " [5/8] CryptoSlate: 10 documents\n",
- " [6/8] CryptoNews: 20 documents\n",
- "\n",
- "Ingesting from 3 web sources...\n",
- " Warning: Blockchain.com Stats failed: 'WebIngestor' object has no attribute 'ingest'\n",
- " Warning: Etherscan failed: 'WebIngestor' object has no attribute 'ingest'\n",
- "\n",
- "✅ Total ingested: 156 documents\n",
- " Sources: 6 unique sources\n",
- " Source list: CoinDesk, CoinTelegraph, CryptoNews, CryptoSlate, Decrypt, The Block\n"
- ]
- }
- ],
- "source": [
- "from semantica.ingest import WebIngestor, FileIngestor, FeedIngestor\n",
- "import os\n",
- "from contextlib import redirect_stderr\n",
- "from io import StringIO\n",
- "\n",
- "os.makedirs(\"data\", exist_ok=True)\n",
- "\n",
- "# Blockchain and crypto news RSS feeds\n",
- "feed_sources = [\n",
- " (\"CoinDesk\", \"https://www.coindesk.com/arc/outboundfeeds/rss/\"),\n",
- " (\"CoinTelegraph\", \"https://cointelegraph.com/rss\"),\n",
- " (\"Decrypt\", \"https://decrypt.co/feed\"),\n",
- " (\"The Block\", \"https://www.theblock.co/rss.xml\"),\n",
- " (\"CryptoSlate\", \"https://cryptoslate.com/feed/\"),\n",
- " (\"CryptoNews\", \"https://cryptonews.com/news/feed/\"),\n",
- " (\"Bitcoin Magazine\", \"https://bitcoinmagazine.com/.rss/full/\"),\n",
- " (\"Ethereum News\", \"https://ethereum.org/en/feed.xml\"),\n",
- "]\n",
- "\n",
- "# Blockchain data and analytics web sources\n",
- "web_sources = [\n",
- " (\"Blockchain.com Stats\", \"https://www.blockchain.com/explorer\"),\n",
- " (\"Etherscan\", \"https://etherscan.io/\"),\n",
- " (\"Bitcoin Explorer\", \"https://blockstream.info/\"),\n",
- "]\n",
- "\n",
- "# Initialize ingestors\n",
- "feed_ingestor = FeedIngestor()\n",
- "web_ingestor = WebIngestor()\n",
- "all_documents = []\n",
- "\n",
- "# Ingest from RSS feeds\n",
- "print(f\"Ingesting from {len(feed_sources)} RSS feed sources...\")\n",
- "for i, (feed_name, feed_url) in enumerate(feed_sources, 1):\n",
- " try:\n",
- " with redirect_stderr(StringIO()):\n",
- " feed_data = feed_ingestor.ingest_feed(feed_url, validate=False)\n",
- " \n",
- " feed_count = 0\n",
- " for item in feed_data.items:\n",
- " if not item.content:\n",
- " item.content = item.description or item.title or \"\"\n",
- " if item.content:\n",
- " if not hasattr(item, 'metadata'):\n",
- " item.metadata = {}\n",
- " item.metadata['source'] = feed_name\n",
- " item.metadata['type'] = 'feed'\n",
- " all_documents.append(item)\n",
- " feed_count += 1\n",
- " \n",
- " if feed_count > 0:\n",
- " print(f\" [{i}/{len(feed_sources)}] {feed_name}: {feed_count} documents\")\n",
- " except Exception as e:\n",
- " if i <= 3: # Show first few errors\n",
- " print(f\" Warning: {feed_name} failed: {str(e)[:50]}\")\n",
- " continue\n",
- "\n",
- "# Ingest from web sources (transaction-related pages)\n",
- "print(f\"\\nIngesting from {len(web_sources)} web sources...\")\n",
- "for i, (web_name, web_url) in enumerate(web_sources, 1):\n",
- " try:\n",
- " with redirect_stderr(StringIO()):\n",
- " web_documents = web_ingestor.ingest(web_url, method=\"url\")\n",
- " \n",
- " web_count = 0\n",
- " for doc in web_documents:\n",
- " if not hasattr(doc, 'metadata'):\n",
- " doc.metadata = {}\n",
- " doc.metadata['source'] = web_name\n",
- " doc.metadata['type'] = 'web'\n",
- " all_documents.append(doc)\n",
- " web_count += 1\n",
- " \n",
- " if web_count > 0:\n",
- " print(f\" [{i}/{len(web_sources)}] {web_name}: {web_count} documents\")\n",
- " except Exception as e:\n",
- " if i <= 2: # Show first few errors\n",
- " print(f\" Warning: {web_name} failed: {str(e)[:50]}\")\n",
- " continue\n",
- "\n",
- "# Fallback to sample transaction data if no documents ingested\n",
- "if not all_documents:\n",
- " print(\"\\n⚠️ No documents ingested from feeds/web sources. Using sample transaction data...\")\n",
- " tx_data = \"\"\"\n",
- " Transaction 0x123 transfers 1000 ETH from wallet 0xABC to wallet 0xDEF at block 18500000.\n",
- " Transaction 0x456 transfers 500 BTC from wallet 0xGHI to wallet 0xJKL at block 18500001.\n",
- " Large transaction 0x789 moves 10000 ETH (whale movement) from wallet 0xMNO to wallet 0xPQR at block 18500002.\n",
- " Transaction 0xabc transfers 200 USDT from wallet 0xSTU to wallet 0xVWX at block 18500003.\n",
- " Transaction 0xdef transfers 5000 ETH from wallet 0xYZA to wallet 0xBCD at block 18500004.\n",
- " Transaction 0x111 transfers 3000 DAI from wallet 0xEFG to wallet 0xHIJ at block 18500005.\n",
- " Transaction 0x222 transfers 1500 USDC from wallet 0xKLM to wallet 0xNOP at block 18500006.\n",
- " Transaction 0x333 transfers 2500 LINK from wallet 0xQRS to wallet 0xTUV at block 18500007.\n",
- " Transaction 0x444 transfers 8000 MATIC from wallet 0xWXY to wallet 0xZAB at block 18500008.\n",
- " Transaction 0x555 transfers 12000 UNI from wallet 0xCDE to wallet 0xFGH at block 18500009.\n",
- " \"\"\"\n",
- " with open(\"data/transactions.txt\", \"w\") as f:\n",
- " f.write(tx_data)\n",
- " file_ingestor = FileIngestor()\n",
- " all_documents = file_ingestor.ingest(\"data/transactions.txt\")\n",
- " for doc in all_documents:\n",
- " if not hasattr(doc, 'metadata'):\n",
- " doc.metadata = {}\n",
- " doc.metadata['source'] = 'Sample Data'\n",
- " doc.metadata['type'] = 'sample'\n",
- "\n",
- "documents = all_documents\n",
- "\n",
- "# Count unique sources properly handling different document types\n",
- "unique_sources = set()\n",
- "for d in documents:\n",
- " if hasattr(d, 'metadata') and d.metadata:\n",
- " source = d.metadata.get('source', 'Unknown')\n",
- " unique_sources.add(source)\n",
- " elif isinstance(d, dict) and 'metadata' in d:\n",
- " source = d['metadata'].get('source', 'Unknown')\n",
- " unique_sources.add(source)\n",
- "\n",
- "print(f\"\\n✅ Total ingested: {len(documents)} documents\")\n",
- "print(f\" Sources: {len(unique_sources)} unique sources\")\n",
- "if unique_sources:\n",
- " print(f\" Source list: {', '.join(sorted(unique_sources))}\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Parsing Transaction Documents\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Parsing 156 documents...\n",
- " Parsed 50/156 documents...\n",
- " Parsed 100/156 documents...\n",
- " Parsed 150/156 documents...\n",
- " Parsed 156/156 documents...\n"
- ]
- }
- ],
- "source": [
- "from semantica.parse import DocumentParser\n",
- "\n",
- "parser = DocumentParser()\n",
- "\n",
- "print(f\"Parsing {len(documents)} documents...\")\n",
- "parsed_documents = []\n",
- "for i, doc in enumerate(documents, 1):\n",
- " try:\n",
- " parsed = parser.parse(\n",
- " doc.content if hasattr(doc, 'content') else str(doc),\n",
- " content_type=\"text\"\n",
- " )\n",
- " parsed_documents.append(parsed)\n",
- " except Exception:\n",
- " parsed_documents.append(doc)\n",
- " if i % 50 == 0 or i == len(documents):\n",
- " print(f\" Parsed {i}/{len(documents)} documents...\")\n",
- "\n",
- "documents = parsed_documents\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Normalizing and Chunking Transaction Data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Normalizing 156 documents...\n",
- " Normalized 50/156 documents...\n",
- " Normalized 100/156 documents...\n",
- " Normalized 150/156 documents...\n",
- " Normalized 156/156 documents...\n",
- "Chunking 156 documents...\n",
- " Chunked 50/156 documents (50 chunks so far)\n",
- " Chunked 100/156 documents (101 chunks so far)\n",
- " Chunked 150/156 documents (151 chunks so far)\n",
- " Chunked 156/156 documents (157 chunks so far)\n",
- "Created 157 chunks from 156 documents\n"
- ]
- }
- ],
- "source": [
- "from semantica.normalize import TextNormalizer\n",
- "from semantica.split import TextSplitter\n",
- "\n",
- "normalizer = TextNormalizer()\n",
- "splitter = TextSplitter(\n",
- " method=\"entity_aware\",\n",
- " ner_method=\"spacy\",\n",
- " chunk_size=CHUNK_SIZE,\n",
- " chunk_overlap=CHUNK_OVERLAP\n",
- ")\n",
- "\n",
- "print(f\"Normalizing {len(documents)} documents...\")\n",
- "normalized_documents = []\n",
- "for i, doc in enumerate(documents, 1):\n",
- " normalized_text = normalizer.normalize(\n",
- " doc.content if hasattr(doc, 'content') else str(doc),\n",
- " clean_html=True,\n",
- " normalize_entities=True,\n",
- " remove_extra_whitespace=True,\n",
- " lowercase=False\n",
- " )\n",
- " normalized_documents.append(normalized_text)\n",
- " if i % 50 == 0 or i == len(documents):\n",
- " print(f\" Normalized {i}/{len(documents)} documents...\")\n",
- "\n",
- "print(f\"Chunking {len(normalized_documents)} documents...\")\n",
- "chunked_documents = []\n",
- "for i, doc_text in enumerate(normalized_documents, 1):\n",
- " try:\n",
- " with redirect_stderr(StringIO()):\n",
- " chunks = splitter.split(doc_text)\n",
- " chunked_documents.extend(chunks)\n",
- " except Exception:\n",
- " simple_splitter = TextSplitter(method=\"recursive\", chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)\n",
- " chunks = simple_splitter.split(doc_text)\n",
- " chunked_documents.extend(chunks)\n",
- " if i % 50 == 0 or i == len(normalized_documents):\n",
- " print(f\" Chunked {i}/{len(normalized_documents)} documents ({len(chunked_documents)} chunks so far)\")\n",
- "\n",
- "print(f\"Created {len(chunked_documents)} chunks from {len(normalized_documents)} documents\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Extracting Transaction Entities\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting entities from 157 chunks using ML (spaCy)...\n",
- "\n",
- "✅ Extraction complete!\n",
- " Total entities: 2420\n",
- " Standard labels: ['DATE', 'CARDINAL', 'ORG', 'GPE', 'PERSON', 'FAC', 'MONEY', 'NORP', 'LOC', 'ORDINAL', 'PRODUCT', 'LAW', 'PERCENT', 'WORK_OF_ART', 'EVENT', 'TIME']\n",
- " Transactions (filtered): 161\n",
- " Wallets/Addresses (filtered): 10\n",
- " Blocks (filtered): 400\n"
- ]
- }
- ],
- "source": [
- "from semantica.semantic_extract import NERExtractor\n",
- "\n",
- "# Initialize NERExtractor with ML method only (spaCy)\n",
- "# Note: ML method extracts standard NER labels (PERSON, ORG, GPE, MONEY, etc.)\n",
- "entity_extractor = NERExtractor(\n",
- " method=[\"ml\"],\n",
- " min_confidence=0.5\n",
- ")\n",
- "\n",
- "# Extract all entities using Semantica's extract() method - handles batch processing\n",
- "print(f\"Extracting entities from {len(chunked_documents)} chunks using ML (spaCy)...\")\n",
- "batch_results = entity_extractor.extract(chunked_documents)\n",
- "\n",
- "# Flatten results (extract() returns List[List[Entity]] for batch input)\n",
- "all_entities = [entity for entity_list in batch_results for entity in entity_list]\n",
- "\n",
- "# Use Semantica's classify_entities to group by standard labels\n",
- "classified = entity_extractor.classify_entities(all_entities)\n",
- "\n",
- "# Filter entities for blockchain transaction domain\n",
- "# Look for transaction hashes, wallet addresses, block numbers, and crypto tokens\n",
- "transaction_keywords = [\"transaction\", \"tx\", \"0x\", \"transfer\", \"sent\", \"received\"]\n",
- "wallet_keywords = [\"wallet\", \"address\", \"0x\", \"account\"]\n",
- "block_keywords = [\"block\", \"blockchain\", \"height\", \"block number\"]\n",
- "\n",
- "transactions = [\n",
- " e for e in all_entities \n",
- " if any(kw in e.text.lower() for kw in transaction_keywords) \n",
- " or e.label == \"MONEY\" # Money entities often represent transactions\n",
- "]\n",
- "wallets = [\n",
- " e for e in all_entities \n",
- " if any(kw in e.text.lower() for kw in wallet_keywords)\n",
- " or (len(e.text) >= 26 and e.text.startswith(\"0x\")) # Ethereum addresses\n",
- "]\n",
- "blocks = [\n",
- " e for e in all_entities \n",
- " if any(kw in e.text.lower() for kw in block_keywords)\n",
- " or e.label == \"CARDINAL\" # Block numbers are often cardinal numbers\n",
- "]\n",
- "\n",
- "print(f\"\\n✅ Extraction complete!\")\n",
- "print(f\" Total entities: {len(all_entities)}\")\n",
- "print(f\" Standard labels: {list(classified.keys())}\")\n",
- "print(f\" Transactions (filtered): {len(transactions)}\")\n",
- "print(f\" Wallets/Addresses (filtered): {len(wallets)}\")\n",
- "print(f\" Blocks (filtered): {len(blocks)}\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Extracting Transaction Relationships\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting relationships from 157 chunks using ML (dependency parsing)...\n",
- " Processed 20/157 chunks (38 relationships found)\n",
- " Processed 40/157 chunks (103 relationships found)\n",
- " Processed 60/157 chunks (174 relationships found)\n",
- " Processed 80/157 chunks (218 relationships found)\n",
- " Processed 100/157 chunks (349 relationships found)\n",
- " Processed 120/157 chunks (443 relationships found)\n",
- " Processed 140/157 chunks (573 relationships found)\n",
- " Processed 157/157 chunks (656 relationships found)\n",
- "\n",
- "✅ Extracted 656 relationships\n"
- ]
- }
- ],
- "source": [
- "from semantica.semantic_extract import RelationExtractor\n",
- "\n",
- "# Use ML-based dependency parsing to avoid rate limits\n",
- "relation_extractor = RelationExtractor(\n",
- " method=\"dependency\", # ML/NLP method - no API calls needed\n",
- " verbose=True\n",
- ")\n",
- "\n",
- "all_relationships = []\n",
- "error_count = 0\n",
- "print(f\"Extracting relationships from {len(chunked_documents)} chunks using ML (dependency parsing)...\")\n",
- "\n",
- "for i, chunk in enumerate(chunked_documents, 1):\n",
- " chunk_text = chunk.text if hasattr(chunk, 'text') else str(chunk)\n",
- " try:\n",
- " relationships = relation_extractor.extract_relations(\n",
- " chunk_text,\n",
- " entities=all_entities,\n",
- " relation_types=[\"transfers\", \"from\", \"to\", \"in_block\", \"contains\", \"flows_to\"],\n",
- " verbose=True\n",
- " )\n",
- " all_relationships.extend(relationships)\n",
- " except Exception as e:\n",
- " error_count += 1\n",
- " if error_count <= 3:\n",
- " print(f\" Warning: Error on chunk {i}: {str(e)[:100]}\")\n",
- " \n",
- " if i % 20 == 0 or i == len(chunked_documents):\n",
- " print(f\" Processed {i}/{len(chunked_documents)} chunks ({len(all_relationships)} relationships found)\")\n",
- "\n",
- "if error_count > 0:\n",
- " print(f\" Note: {error_count} chunks had errors during relation extraction\")\n",
- "\n",
- "print(f\"\\n✅ Extracted {len(all_relationships)} relationships\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Detecting Transaction Conflicts\n",
- "\n",
- "- **Multi-Type Detection**: Detects entity, relationship, and temporal conflicts across transaction network\n",
- "- **Most Recent Strategy**: Uses `most_recent` resolution for transaction data (most accurate for blockchain)\n",
- "- **Source-Aware Resolution**: Considers source reliability and confidence scores for conflict resolution\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detecting conflicts in 2420 entities and 656 relationships...\n",
- "Detected 0 total conflicts\n",
- "✅ No conflicts detected - data is consistent\n"
- ]
- }
- ],
- "source": [
- "from semantica.conflicts import ConflictDetector, ConflictResolver\n",
- "\n",
- "# Initialize conflict detection and resolution\n",
- "conflict_detector = ConflictDetector()\n",
- "conflict_resolver = ConflictResolver()\n",
- "\n",
- "# Use Semantica's conflict detection methods directly\n",
- "# Detects entity, relationship, and temporal conflicts\n",
- "print(f\"Detecting conflicts in {len(all_entities)} entities and {len(all_relationships)} relationships...\")\n",
- "\n",
- "# Convert to dict format for conflict detection (Semantica expects dicts)\n",
- "entity_dicts = [{\"id\": e.text, \"text\": e.text, \"type\": e.label, \"confidence\": getattr(e, 'confidence', 1.0)} for e in all_entities]\n",
- "relationship_dicts = [{\"id\": f\"{r.subject.text}_{r.predicate}_{r.object.text}\", \"source_id\": r.subject.text, \"target_id\": r.object.text, \"type\": r.predicate} for r in all_relationships]\n",
- "\n",
- "# Detect all conflict types using Semantica's methods\n",
- "all_conflicts = []\n",
- "all_conflicts.extend(conflict_detector.detect_entity_conflicts(entity_dicts))\n",
- "all_conflicts.extend(conflict_detector.detect_relationship_conflicts(relationship_dicts))\n",
- "all_conflicts.extend(conflict_detector.detect_temporal_conflicts(entity_dicts))\n",
- "\n",
- "print(f\"Detected {len(all_conflicts)} total conflicts\")\n",
- "\n",
- "# Resolve conflicts using best strategy for transaction networks\n",
- "if all_conflicts:\n",
- " print(f\"Resolving conflicts using 'most_recent' strategy (best for transaction data)...\")\n",
- " resolved = conflict_resolver.resolve_conflicts(all_conflicts, strategy=\"most_recent\")\n",
- " resolved_count = len([r for r in resolved if r.resolved])\n",
- " print(f\"✅ Resolved {resolved_count}/{len(all_conflicts)} conflicts\")\n",
- "else:\n",
- " print(\"✅ No conflicts detected - data is consistent\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Building Temporal Transaction Network Graph\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Processing 2420 entities, 656 relationships (3076 total)...\n",
- " Entities: 121/2420 (5.0%) | ETA: 41.3m | Rate: 0.9/s\n",
- " Entities: 242/2420 (10.0%) | ETA: 40.3m | Rate: 0.9/s\n",
- " Entities: 363/2420 (15.0%) | ETA: 38.0m | Rate: 0.9/s\n",
- " Entities: 484/2420 (20.0%) | ETA: 35.7m | Rate: 0.9/s\n",
- " Entities: 605/2420 (25.0%) | ETA: 33.4m | Rate: 0.9/s\n",
- " Entities: 726/2420 (30.0%) | ETA: 31.4m | Rate: 0.9/s\n",
- " Entities: 847/2420 (35.0%) | ETA: 29.1m | Rate: 0.9/s\n",
- " Entities: 968/2420 (40.0%) | ETA: 27.9m | Rate: 0.9/s\n",
- " Entities: 1089/2420 (45.0%) | ETA: 25.5m | Rate: 0.9/s\n",
- " Entities: 1210/2420 (50.0%) | ETA: 23.2m | Rate: 0.9/s\n",
- " Entities: 1331/2420 (55.0%) | ETA: 20.9m | Rate: 0.9/s\n",
- " Entities: 1452/2420 (60.0%) | ETA: 18.6m | Rate: 0.9/s\n",
- " Entities: 1573/2420 (65.0%) | ETA: 16.2m | Rate: 0.9/s\n",
- " Entities: 1694/2420 (70.0%) | ETA: 13.9m | Rate: 0.9/s\n",
- " Entities: 1815/2420 (75.0%) | ETA: 11.7m | Rate: 0.9/s\n",
- " Entities: 1936/2420 (80.0%) | ETA: 9.3m | Rate: 0.9/s\n",
- " Entities: 2057/2420 (85.0%) | ETA: 7.0m | Rate: 0.9/s\n",
- " Entities: 2178/2420 (90.0%) | ETA: 4.7m | Rate: 0.9/s\n",
- " Entities: 2299/2420 (95.0%) | ETA: 2.4m | Rate: 0.9/s\n",
- " Entities: 2420/2420 (100.0%) | ETA: 0.0s | Rate: 0.9/s\n",
- " Relationships: 100/656\n",
- " Relationships: 200/656\n",
- " Relationships: 300/656\n",
- " Relationships: 400/656\n",
- " Relationships: 500/656\n",
- " Relationships: 600/656\n",
- " Relationships: 656/656\n",
- "Building graph structure...\n",
- "✅ Graph structure built (0.00s)\n",
- "\n",
- "============================================================\n",
- "✅ Knowledge Graph Build Complete\n",
- " Entities: 2131\n",
- " Relationships: 656\n",
- " Total time: 2835.60s\n",
- "============================================================\n"
- ]
- }
- ],
- "source": [
- "from semantica.kg import GraphBuilder\n",
- "\n",
- "# Conflicts already resolved - disable expensive operations\n",
- "graph_builder = GraphBuilder(\n",
- " merge_entities=False, # Skip entity merging (already done in conflict resolution)\n",
- " resolve_conflicts=False, # Conflicts already resolved\n",
- " entity_resolution_strategy=\"exact\", \n",
- " enable_temporal=True,\n",
- " temporal_granularity=TEMPORAL_GRANULARITY,\n",
- " track_history=True, \n",
- " version_snapshots=True \n",
- ")\n",
- "\n",
- "# Build graph - Semantica's build() method automatically shows progress and ETA\n",
- "kg_sources = [{\n",
- " \"entities\": [{\"text\": e.text, \"type\": e.label, \"confidence\": getattr(e, 'confidence', 1.0)} for e in all_entities],\n",
- " \"relationships\": [{\"source\": r.subject.text, \"target\": r.object.text, \"type\": r.predicate, \"confidence\": getattr(r, 'confidence', 1.0)} for r in all_relationships]\n",
- "}]\n",
- "\n",
- "kg = graph_builder.build(kg_sources)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Generating Embeddings for Transactions and Wallets\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating embeddings for 161 transactions and 10 wallets...\n",
- "Generated 161 transaction embeddings and 10 wallet embeddings\n"
- ]
- }
- ],
- "source": [
- "from semantica.embeddings import EmbeddingGenerator\n",
- "\n",
- "embedding_gen = EmbeddingGenerator(\n",
- " provider=\"sentence_transformers\",\n",
- " model=EMBEDDING_MODEL\n",
- ")\n",
- "\n",
- "print(f\"Generating embeddings for {len(transactions)} transactions and {len(wallets)} wallets...\")\n",
- "transaction_texts = [t.text for t in transactions]\n",
- "transaction_embeddings = embedding_gen.generate_embeddings(transaction_texts)\n",
- "\n",
- "wallet_texts = [w.text for w in wallets]\n",
- "wallet_embeddings = embedding_gen.generate_embeddings(wallet_texts)\n",
- "\n",
- "print(f\"Generated {len(transaction_embeddings)} transaction embeddings and {len(wallet_embeddings)} wallet embeddings\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Populating Vector Store\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Storing 161 transaction vectors and 10 wallet vectors...\n",
- "Stored 161 transaction vectors and 10 wallet vectors\n"
- ]
- }
- ],
- "source": [
- "from semantica.vector_store import VectorStore\n",
- "\n",
- "vector_store = VectorStore(backend=\"faiss\", dimension=EMBEDDING_DIMENSION)\n",
- "\n",
- "print(f\"Storing {len(transaction_embeddings)} transaction vectors and {len(wallet_embeddings)} wallet vectors...\")\n",
- "transaction_ids = vector_store.store_vectors(\n",
- " vectors=transaction_embeddings,\n",
- " metadata=[{\"type\": \"transaction\", \"name\": t.text, \"label\": t.label} for t in transactions]\n",
- ")\n",
- "\n",
- "wallet_ids = vector_store.store_vectors(\n",
- " vectors=wallet_embeddings,\n",
- " metadata=[{\"type\": \"wallet\", \"name\": w.text, \"label\": w.label} for w in wallets]\n",
- ")\n",
- "\n",
- "print(f\"Stored {len(transaction_ids)} transaction vectors and {len(wallet_ids)} wallet vectors\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Analyzing Graph Structure\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Graph analytics:\n",
- " - Communities: 4\n",
- " - Connected components: 2\n",
- " - Graph density: 0.000\n",
- " - Central nodes (degree): 4\n"
- ]
- }
- ],
- "source": [
- "from semantica.kg import GraphAnalyzer, CentralityCalculator, CommunityDetector\n",
- "\n",
- "graph_analyzer = GraphAnalyzer()\n",
- "centrality_calc = CentralityCalculator()\n",
- "community_detector = CommunityDetector()\n",
- "\n",
- "analysis = graph_analyzer.analyze_graph(kg)\n",
- "\n",
- "degree_centrality = centrality_calc.calculate_degree_centrality(kg)\n",
- "betweenness_centrality = centrality_calc.calculate_betweenness_centrality(kg)\n",
- "closeness_centrality = centrality_calc.calculate_closeness_centrality(kg)\n",
- "\n",
- "communities = community_detector.detect_communities(kg, method=\"louvain\")\n",
- "connectivity = graph_analyzer.analyze_connectivity(kg)\n",
- "\n",
- "print(f\"Graph analytics:\")\n",
- "print(f\" - Communities: {len(communities)}\")\n",
- "print(f\" - Connected components: {len(connectivity.get('components', []))}\")\n",
- "print(f\" - Graph density: {analysis.get('density', 0):.3f}\")\n",
- "print(f\" - Central nodes (degree): {len(degree_centrality)}\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Temporal Graph Queries\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Temporal queries: 656 relationships at query time\n",
- "Temporal patterns detected: 0\n"
- ]
- }
- ],
- "source": [
- "from semantica.kg import TemporalGraphQuery\n",
- "\n",
- "temporal_query = TemporalGraphQuery(\n",
- " enable_temporal_reasoning=True,\n",
- " temporal_granularity=TEMPORAL_GRANULARITY\n",
- ")\n",
- "\n",
- "query_results = temporal_query.query_at_time(\n",
- " kg,\n",
- " query={\"type\": \"Transaction\"},\n",
- " at_time=\"2024-01-01\"\n",
- ")\n",
- "\n",
- "evolution = temporal_query.analyze_evolution(kg)\n",
- "temporal_patterns = temporal_query.query_temporal_pattern(kg, pattern=\"sequence\")\n",
- "\n",
- "print(f\"Temporal queries: {query_results.get('num_relationships', 0)} relationships at query time\")\n",
- "print(f\"Temporal patterns detected: {temporal_patterns.get('num_patterns', 0)}\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Detecting Patterns and Whale Movements\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Whale tracking: 0 large transaction wallets identified\n",
- "Suspicious patterns: 0 high-frequency wallets\n"
- ]
- }
- ],
- "source": [
- "\n",
- "# Detect whale movements (large transactions)\n",
- "whale_wallets = []\n",
- "for entity in kg.get(\"entities\", []):\n",
- " if entity.get(\"type\") in [\"Wallet\", \"Address\"]:\n",
- " # Check for large transaction relationships\n",
- " related_rels = [r for r in kg.get(\"relationships\", []) \n",
- " if r.get(\"source\") == entity.get(\"id\") or r.get(\"target\") == entity.get(\"id\")]\n",
- " if any(\"large\" in str(r.get(\"type\", \"\")).lower() or \"whale\" in str(r.get(\"type\", \"\")).lower() \n",
- " for r in related_rels):\n",
- " whale_wallets.append(entity)\n",
- "\n",
- "# Detect suspicious patterns (high frequency transactions)\n",
- "suspicious_patterns = []\n",
- "for wallet in wallets[:10]:\n",
- " wallet_name = wallet.text\n",
- " # Find all relationships where wallet is source or target, and connected to Transaction entities\n",
- " wallet_relationships = [\n",
- " r for r in kg.get(\"relationships\", [])\n",
- " if (r.get(\"source\") == wallet_name or r.get(\"target\") == wallet_name)\n",
- " ]\n",
- " # Find connected Transaction entities\n",
- " transaction_ids = set()\n",
- " for rel in wallet_relationships:\n",
- " other_entity_id = rel.get(\"target\") if rel.get(\"source\") == wallet_name else rel.get(\"source\")\n",
- " # Check if the other entity is a Transaction\n",
- " other_entity = next((e for e in kg.get(\"entities\", []) if e.get(\"id\") == other_entity_id), None)\n",
- " if other_entity and other_entity.get(\"type\") == \"Transaction\":\n",
- " transaction_ids.add(other_entity_id)\n",
- " \n",
- " transaction_count = len(transaction_ids)\n",
- " if transaction_count > 5: # High transaction frequency\n",
- " suspicious_patterns.append({\n",
- " 'wallet': wallet_name,\n",
- " 'transaction_count': transaction_count\n",
- " })\n",
- "\n",
- "print(f\"Whale tracking: {len(whale_wallets)} large transaction wallets identified\")\n",
- "print(f\"Suspicious patterns: {len(suspicious_patterns)} high-frequency wallets\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Analyzing Token Flows\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Flow analysis: 0 token flow paths identified\n"
- ]
- }
- ],
- "source": [
- "# Analyze token flows through the network\n",
- "from collections import deque\n",
- "\n",
- "flow_analysis = []\n",
- "for transaction in transactions[:10]:\n",
- " tx_name = transaction.text\n",
- " \n",
- " # Build adjacency list from relationships\n",
- " adjacency = {}\n",
- " for rel in kg.get(\"relationships\", []):\n",
- " source = rel.get(\"source\")\n",
- " target = rel.get(\"target\")\n",
- " if source and target:\n",
- " if source not in adjacency:\n",
- " adjacency[source] = []\n",
- " if target not in adjacency[source]:\n",
- " adjacency[source].append(target)\n",
- " if target not in adjacency:\n",
- " adjacency[target] = []\n",
- " if source not in adjacency[target]:\n",
- " adjacency[target].append(source)\n",
- " \n",
- " # BFS to find wallets within 2 hops\n",
- " if tx_name not in adjacency:\n",
- " continue\n",
- " \n",
- " queue = deque([(tx_name, [tx_name], 0)])\n",
- " visited = {tx_name}\n",
- " paths_to_wallets = []\n",
- " \n",
- " while queue:\n",
- " node, path, hops = queue.popleft()\n",
- " \n",
- " if hops > 2: # Max 2 hops\n",
- " continue\n",
- " \n",
- " # Check if current node is a Wallet or Address\n",
- " entity = next((e for e in kg.get(\"entities\", []) if e.get(\"id\") == node), None)\n",
- " if entity and entity.get(\"type\") in [\"Wallet\", \"Address\"] and node != tx_name:\n",
- " paths_to_wallets.append({\n",
- " 'transaction': tx_name,\n",
- " 'flow_path': path,\n",
- " 'target': node,\n",
- " 'path_length': len(path) - 1\n",
- " })\n",
- " \n",
- " # Continue BFS\n",
- " for neighbor in adjacency.get(node, []):\n",
- " if neighbor not in visited:\n",
- " visited.add(neighbor)\n",
- " queue.append((neighbor, path + [neighbor], hops + 1))\n",
- " \n",
- " flow_analysis.extend(paths_to_wallets)\n",
- "\n",
- "flow_analysis.sort(key=lambda x: x['path_length'])\n",
- "\n",
- "print(f\"Flow analysis: {len(flow_analysis)} token flow paths identified\")\n",
- "for i, flow in enumerate(flow_analysis[:5], 1):\n",
- " print(f\"{i}. {flow['transaction']} -> {flow['target']} (path length: {flow['path_length']})\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Storing Transaction Network (Optional)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Graph store configured (commented out for demo)\n"
- ]
- }
- ],
- "source": [
- "from semantica.graph_store import GraphStore\n",
- "\n",
- "# Optional: Store to persistent graph database\n",
- "# graph_store = GraphStore(backend=\"neo4j\", uri=\"bolt://localhost:7687\", user=\"neo4j\", password=\"password\")\n",
- "# graph_store.store_graph(kg)\n",
- "\n",
- "print(\"Graph store configured (commented out for demo)\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## GraphRAG: Hybrid Vector + Graph Queries\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Embedding generation failed: Text cannot be empty or whitespace-only\n",
- "Using random fallback embedding\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "GraphRAG query: 'What are the largest transactions?'\n",
- "\n",
- "Retrieved 10 results:\n",
- "\n",
- "1. Score: 0.574\n",
- " Content: Entity(text='canton is a ORG....\n",
- "\n",
- "2. Score: 0.500\n",
- " Content: ...\n",
- "\n",
- "3. Score: 0.475\n",
- " Content: Entity(text='Hong kong' is a GPE....\n",
- "\n",
- "4. Score: 0.329\n",
- " Content: HPC is a ORG. identify the Cubic Kilometre Neutrino (ORG). be Vanguard ETFs (PERSON), CoinDesk....\n",
- " Related entities: 2\n",
- "\n",
- "5. Score: 0.324\n",
- " Content: nearly 20% is a PERCENT....\n",
- "\n"
- ]
- }
- ],
- "source": [
- "from semantica.context import AgentContext\n",
- "\n",
- "context = AgentContext(vector_store=vector_store, knowledge_graph=kg)\n",
- "\n",
- "query = \"What are the largest transactions?\"\n",
- "results = context.retrieve(\n",
- " query,\n",
- " max_results=10,\n",
- " use_graph=True,\n",
- " expand_graph=True,\n",
- " include_entities=True,\n",
- " include_relationships=True\n",
- ")\n",
- "\n",
- "print(f\"GraphRAG query: '{query}'\")\n",
- "print(f\"\\nRetrieved {len(results)} results:\\n\")\n",
- "for i, result in enumerate(results[:5], 1):\n",
- " print(f\"{i}. Score: {result.get('score', 0):.3f}\")\n",
- " print(f\" Content: {result.get('content', '')[:200]}...\")\n",
- " if result.get('related_entities'):\n",
- " print(f\" Related entities: {len(result['related_entities'])}\")\n",
- " print()\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Visualizing the Transaction Network\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
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- "bgcolor": "#E5ECF6",
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- "title": {
- "text": "Knowledge Graph Network"
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- "xaxis": {
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- "metadata": {},
- "output_type": "display_data"
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- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Interactive visualization displayed above\n",
- "📁 Visualization also saved to transaction_network.html\n"
- ]
- }
- ],
- "source": [
- "from semantica.visualization import KGVisualizer\n",
- "\n",
- "# Create visualizer with force-directed layout for better interactive visualization\n",
- "visualizer = KGVisualizer(\n",
- " layout=\"force\", # Force-directed layout for better node distribution\n",
- " color_scheme=\"vibrant\", # Better color scheme\n",
- " node_size=15,\n",
- " edge_width=1.5\n",
- ")\n",
- "\n",
- "# Create interactive network visualization\n",
- "fig = visualizer.visualize_network(\n",
- " kg,\n",
- " output=\"interactive\", # Interactive Plotly visualization\n",
- " file_path=\"transaction_network.html\", # Also save to HTML file\n",
- " node_color_by=\"type\", # Color nodes by entity type\n",
- " hover_data=[\"type\", \"label\"] # Show type and label in hover tooltip\n",
- ")\n",
- "\n",
- "# Display the interactive figure in the notebook\n",
- "fig.show()\n",
- "\n",
- "print(\"✅ Interactive visualization displayed above\")\n",
- "print(\"📁 Visualization also saved to transaction_network.html\")\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Exporting Results\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "✅ Exported transaction network to multiple formats:\n",
- " - JSON: transaction_network.json\n",
- " - GraphML: transaction_network.graphml\n",
- " - GEXF: transaction_network.gexf\n",
- " - DOT: transaction_network.dot\n",
- " - CSV: transaction_network.csv\n"
- ]
- }
- ],
- "source": [
- "from semantica.export import GraphExporter, export_csv\n",
- "\n",
- "# Export to graph formats using GraphExporter\n",
- "graph_exporter = GraphExporter()\n",
- "graph_exporter.export(kg, file_path=\"transaction_network.json\", format=\"json\")\n",
- "graph_exporter.export(kg, file_path=\"transaction_network.graphml\", format=\"graphml\")\n",
- "graph_exporter.export(kg, file_path=\"transaction_network.gexf\", format=\"gexf\")\n",
- "graph_exporter.export(kg, file_path=\"transaction_network.dot\", format=\"dot\")\n",
- "\n",
- "# Export to CSV using export_csv convenience function\n",
- "# This creates separate CSV files for entities and relationships\n",
- "export_csv(kg, \"transaction_network\")\n",
- "\n",
- "print(\"✅ Exported transaction network to multiple formats:\")\n",
- "print(\" - JSON: transaction_network.json\")\n",
- "print(\" - GraphML: transaction_network.graphml\")\n",
- "print(\" - GEXF: transaction_network.gexf\")\n",
- "print(\" - DOT: transaction_network.dot\")\n",
- "print(\" - CSV: transaction_network_entities.csv, transaction_network_relationships.csv\")\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
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+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/use_cases/blockchain/02_Transaction_Network_Analysis.ipynb)\n",
+ "\n",
+ "# Transaction Network Analysis - Pattern Detection & Graph Analytics\n",
+ "\n",
+ "## Overview\n",
+ "\n",
+ "This notebook demonstrates **blockchain transaction network analysis** using Semantica with focus on **pattern detection**, **network analytics**, and **real-time processing**. The pipeline analyzes blockchain transaction networks to detect patterns, identify whale movements, and analyze token flows.\n",
+ "\n",
+ "### Key Features\n",
+ "\n",
+ "- **Pattern Detection**: Emphasizes graph analytics for transaction pattern recognition\n",
+ "- **Network Analytics**: Uses centrality measures and community detection\n",
+ "- **Temporal Analysis**: Time-aware queries and transaction evolution tracking\n",
+ "- **Whale Tracking**: Identifies large transaction movements\n",
+ "- **Flow Analysis**: Analyzes token flows through the network\n",
+ "- **Comprehensive Data Sources**: Multiple blockchain APIs, analytics platforms, and databases\n",
+ "\n",
+ "### Learning Objectives\n",
+ "\n",
+ "- Ingest blockchain transaction data from multiple sources\n",
+ "- Extract transaction entities (Transactions, Wallets, Addresses, Blocks, Flows)\n",
+ "- Build temporal transaction network graphs\n",
+ "- Perform graph analytics (centrality, communities, connectivity)\n",
+ "- Detect patterns and whale movements\n",
+ "- Analyze token flows and transaction paths\n",
+ "- Store and query transaction data using vector stores and graph stores\n",
+ "\n",
+ "### Pipeline Flow\n",
+ "\n",
+ "```mermaid\n",
+ "graph TD\n",
+ " A[Data Ingestion] --> B[Document Parsing]\n",
+ " B --> C[Text Processing]\n",
+ " C --> D[Entity Extraction]\n",
+ " D --> E[Relationship Extraction]\n",
+ " E --> F[Deduplication]\n",
+ " F --> G[Conflict Detection]\n",
+ " G --> H[Transaction Network Graph]\n",
+ " H --> I[Embeddings]\n",
+ " I --> J[Vector Store]\n",
+ " H --> K[Graph Analytics]\n",
+ " K --> L[Temporal Queries]\n",
+ " L --> M[Pattern Detection]\n",
+ " M --> N[Flow Analysis]\n",
+ " J --> O[GraphRAG Queries]\n",
+ " H --> P[Graph Store]\n",
+ " O --> Q[Visualization]\n",
+ " P --> Q\n",
+ " Q --> R[Export]\n",
+ "```\n",
+ "\n",
+ "## Installation\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 2
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq sentence-transformers scikit-learn\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Configuration & Setup\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"GROQ_API_KEY\"] = os.getenv(\"GROQ_API_KEY\", \"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\")\n",
+ "\n",
+ "# Configuration constants\n",
+ "EMBEDDING_DIMENSION = 384\n",
+ "EMBEDDING_MODEL = \"all-MiniLM-L6-v2\"\n",
+ "CHUNK_SIZE = 1000\n",
+ "CHUNK_OVERLAP = 200\n",
+ "TEMPORAL_GRANULARITY = \"day\"\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ingesting Blockchain Transaction Data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.ingest import WebIngestor, FileIngestor, FeedIngestor\n",
+ "import os\n",
+ "from contextlib import redirect_stderr\n",
+ "from io import StringIO\n",
+ "\n",
+ "os.makedirs(\"data\", exist_ok=True)\n",
+ "\n",
+ "# Blockchain and crypto news RSS feeds\n",
+ "feed_sources = [\n",
+ " (\"CoinDesk\", \"https://www.coindesk.com/arc/outboundfeeds/rss/\"),\n",
+ " (\"CoinTelegraph\", \"https://cointelegraph.com/rss\"),\n",
+ " (\"Decrypt\", \"https://decrypt.co/feed\"),\n",
+ " (\"The Block\", \"https://www.theblock.co/rss.xml\"),\n",
+ " (\"CryptoSlate\", \"https://cryptoslate.com/feed/\"),\n",
+ " (\"CryptoNews\", \"https://cryptonews.com/news/feed/\"),\n",
+ " (\"Bitcoin Magazine\", \"https://bitcoinmagazine.com/.rss/full/\"),\n",
+ " (\"Ethereum News\", \"https://ethereum.org/en/feed.xml\"),\n",
+ "]\n",
+ "\n",
+ "# Blockchain data and analytics web sources\n",
+ "web_sources = [\n",
+ " (\"Blockchain.com Stats\", \"https://www.blockchain.com/explorer\"),\n",
+ " (\"Etherscan\", \"https://etherscan.io/\"),\n",
+ " (\"Bitcoin Explorer\", \"https://blockstream.info/\"),\n",
+ "]\n",
+ "\n",
+ "# Initialize ingestors\n",
+ "feed_ingestor = FeedIngestor()\n",
+ "web_ingestor = WebIngestor()\n",
+ "all_documents = []\n",
+ "\n",
+ "# Ingest from RSS feeds\n",
+ "print(f\"Ingesting from {len(feed_sources)} RSS feed sources...\")\n",
+ "for i, (feed_name, feed_url) in enumerate(feed_sources, 1):\n",
+ " try:\n",
+ " with redirect_stderr(StringIO()):\n",
+ " feed_data = feed_ingestor.ingest_feed(feed_url, validate=False)\n",
+ " \n",
+ " feed_count = 0\n",
+ " for item in feed_data.items:\n",
+ " if not item.content:\n",
+ " item.content = item.description or item.title or \"\"\n",
+ " if item.content:\n",
+ " if not hasattr(item, 'metadata'):\n",
+ " item.metadata = {}\n",
+ " item.metadata['source'] = feed_name\n",
+ " item.metadata['type'] = 'feed'\n",
+ " all_documents.append(item)\n",
+ " feed_count += 1\n",
+ " \n",
+ " if feed_count > 0:\n",
+ " print(f\" [{i}/{len(feed_sources)}] {feed_name}: {feed_count} documents\")\n",
+ " except Exception as e:\n",
+ " if i <= 3: # Show first few errors\n",
+ " print(f\" Warning: {feed_name} failed: {str(e)[:50]}\")\n",
+ " continue\n",
+ "\n",
+ "# Ingest from web sources (transaction-related pages)\n",
+ "print(f\"\\nIngesting from {len(web_sources)} web sources...\")\n",
+ "for i, (web_name, web_url) in enumerate(web_sources, 1):\n",
+ " try:\n",
+ " with redirect_stderr(StringIO()):\n",
+ " web_documents = web_ingestor.ingest(web_url, method=\"url\")\n",
+ " \n",
+ " web_count = 0\n",
+ " for doc in web_documents:\n",
+ " if not hasattr(doc, 'metadata'):\n",
+ " doc.metadata = {}\n",
+ " doc.metadata['source'] = web_name\n",
+ " doc.metadata['type'] = 'web'\n",
+ " all_documents.append(doc)\n",
+ " web_count += 1\n",
+ " \n",
+ " if web_count > 0:\n",
+ " print(f\" [{i}/{len(web_sources)}] {web_name}: {web_count} documents\")\n",
+ " except Exception as e:\n",
+ " if i <= 2: # Show first few errors\n",
+ " print(f\" Warning: {web_name} failed: {str(e)[:50]}\")\n",
+ " continue\n",
+ "\n",
+ "# Fallback to sample transaction data if no documents ingested\n",
+ "if not all_documents:\n",
+ " print(\"\\n⚠️ No documents ingested from feeds/web sources. Using sample transaction data...\")\n",
+ " tx_data = \"\"\"\n",
+ " Transaction 0x123 transfers 1000 ETH from wallet 0xABC to wallet 0xDEF at block 18500000.\n",
+ " Transaction 0x456 transfers 500 BTC from wallet 0xGHI to wallet 0xJKL at block 18500001.\n",
+ " Large transaction 0x789 moves 10000 ETH (whale movement) from wallet 0xMNO to wallet 0xPQR at block 18500002.\n",
+ " Transaction 0xabc transfers 200 USDT from wallet 0xSTU to wallet 0xVWX at block 18500003.\n",
+ " Transaction 0xdef transfers 5000 ETH from wallet 0xYZA to wallet 0xBCD at block 18500004.\n",
+ " Transaction 0x111 transfers 3000 DAI from wallet 0xEFG to wallet 0xHIJ at block 18500005.\n",
+ " Transaction 0x222 transfers 1500 USDC from wallet 0xKLM to wallet 0xNOP at block 18500006.\n",
+ " Transaction 0x333 transfers 2500 LINK from wallet 0xQRS to wallet 0xTUV at block 18500007.\n",
+ " Transaction 0x444 transfers 8000 MATIC from wallet 0xWXY to wallet 0xZAB at block 18500008.\n",
+ " Transaction 0x555 transfers 12000 UNI from wallet 0xCDE to wallet 0xFGH at block 18500009.\n",
+ " \"\"\"\n",
+ " with open(\"data/transactions.txt\", \"w\") as f:\n",
+ " f.write(tx_data)\n",
+ " file_ingestor = FileIngestor()\n",
+ " all_documents = file_ingestor.ingest(\"data/transactions.txt\")\n",
+ " for doc in all_documents:\n",
+ " if not hasattr(doc, 'metadata'):\n",
+ " doc.metadata = {}\n",
+ " doc.metadata['source'] = 'Sample Data'\n",
+ " doc.metadata['type'] = 'sample'\n",
+ "\n",
+ "documents = all_documents\n",
+ "\n",
+ "# Count unique sources properly handling different document types\n",
+ "unique_sources = set()\n",
+ "for d in documents:\n",
+ " if hasattr(d, 'metadata') and d.metadata:\n",
+ " source = d.metadata.get('source', 'Unknown')\n",
+ " unique_sources.add(source)\n",
+ " elif isinstance(d, dict) and 'metadata' in d:\n",
+ " source = d['metadata'].get('source', 'Unknown')\n",
+ " unique_sources.add(source)\n",
+ "\n",
+ "print(f\"\\n✅ Total ingested: {len(documents)} documents\")\n",
+ "print(f\" Sources: {len(unique_sources)} unique sources\")\n",
+ "if unique_sources:\n",
+ " print(f\" Source list: {', '.join(sorted(unique_sources))}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Parsing Transaction Documents\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.parse import DocumentParser\n",
+ "\n",
+ "parser = DocumentParser()\n",
+ "\n",
+ "print(f\"Parsing {len(documents)} documents...\")\n",
+ "parsed_documents = []\n",
+ "for i, doc in enumerate(documents, 1):\n",
+ " try:\n",
+ " parsed = parser.parse(\n",
+ " doc.content if hasattr(doc, 'content') else str(doc),\n",
+ " content_type=\"text\"\n",
+ " )\n",
+ " parsed_documents.append(parsed)\n",
+ " except Exception:\n",
+ " parsed_documents.append(doc)\n",
+ " if i % 50 == 0 or i == len(documents):\n",
+ " print(f\" Parsed {i}/{len(documents)} documents...\")\n",
+ "\n",
+ "documents = parsed_documents\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Normalizing and Chunking Transaction Data\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.normalize import TextNormalizer\n",
+ "from semantica.split import TextSplitter\n",
+ "\n",
+ "normalizer = TextNormalizer()\n",
+ "splitter = TextSplitter(\n",
+ " method=\"entity_aware\",\n",
+ " ner_method=\"spacy\",\n",
+ " chunk_size=CHUNK_SIZE,\n",
+ " chunk_overlap=CHUNK_OVERLAP\n",
+ ")\n",
+ "\n",
+ "print(f\"Normalizing {len(documents)} documents...\")\n",
+ "normalized_documents = []\n",
+ "for i, doc in enumerate(documents, 1):\n",
+ " normalized_text = normalizer.normalize(\n",
+ " doc.content if hasattr(doc, 'content') else str(doc),\n",
+ " clean_html=True,\n",
+ " normalize_entities=True,\n",
+ " remove_extra_whitespace=True,\n",
+ " lowercase=False\n",
+ " )\n",
+ " normalized_documents.append(normalized_text)\n",
+ " if i % 50 == 0 or i == len(documents):\n",
+ " print(f\" Normalized {i}/{len(documents)} documents...\")\n",
+ "\n",
+ "print(f\"Chunking {len(normalized_documents)} documents...\")\n",
+ "chunked_documents = []\n",
+ "for i, doc_text in enumerate(normalized_documents, 1):\n",
+ " try:\n",
+ " with redirect_stderr(StringIO()):\n",
+ " chunks = splitter.split(doc_text)\n",
+ " chunked_documents.extend(chunks)\n",
+ " except Exception:\n",
+ " simple_splitter = TextSplitter(method=\"recursive\", chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)\n",
+ " chunks = simple_splitter.split(doc_text)\n",
+ " chunked_documents.extend(chunks)\n",
+ " if i % 50 == 0 or i == len(normalized_documents):\n",
+ " print(f\" Chunked {i}/{len(normalized_documents)} documents ({len(chunked_documents)} chunks so far)\")\n",
+ "\n",
+ "print(f\"Created {len(chunked_documents)} chunks from {len(normalized_documents)} documents\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extracting Transaction Entities\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.semantic_extract import NERExtractor\n",
+ "\n",
+ "# Initialize NERExtractor with ML method only (spaCy)\n",
+ "# Note: ML method extracts standard NER labels (PERSON, ORG, GPE, MONEY, etc.)\n",
+ "entity_extractor = NERExtractor(\n",
+ " method=[\"ml\"],\n",
+ " min_confidence=0.5\n",
+ ")\n",
+ "\n",
+ "# Extract all entities using Semantica's extract() method - handles batch processing\n",
+ "print(f\"Extracting entities from {len(chunked_documents)} chunks using ML (spaCy)...\")\n",
+ "batch_results = entity_extractor.extract(chunked_documents)\n",
+ "\n",
+ "# Flatten results (extract() returns List[List[Entity]] for batch input)\n",
+ "all_entities = [entity for entity_list in batch_results for entity in entity_list]\n",
+ "\n",
+ "# Use Semantica's classify_entities to group by standard labels\n",
+ "classified = entity_extractor.classify_entities(all_entities)\n",
+ "\n",
+ "# Filter entities for blockchain transaction domain\n",
+ "# Look for transaction hashes, wallet addresses, block numbers, and crypto tokens\n",
+ "transaction_keywords = [\"transaction\", \"tx\", \"0x\", \"transfer\", \"sent\", \"received\"]\n",
+ "wallet_keywords = [\"wallet\", \"address\", \"0x\", \"account\"]\n",
+ "block_keywords = [\"block\", \"blockchain\", \"height\", \"block number\"]\n",
+ "\n",
+ "transactions = [\n",
+ " e for e in all_entities \n",
+ " if any(kw in e.text.lower() for kw in transaction_keywords) \n",
+ " or e.label == \"MONEY\" # Money entities often represent transactions\n",
+ "]\n",
+ "wallets = [\n",
+ " e for e in all_entities \n",
+ " if any(kw in e.text.lower() for kw in wallet_keywords)\n",
+ " or (len(e.text) >= 26 and e.text.startswith(\"0x\")) # Ethereum addresses\n",
+ "]\n",
+ "blocks = [\n",
+ " e for e in all_entities \n",
+ " if any(kw in e.text.lower() for kw in block_keywords)\n",
+ " or e.label == \"CARDINAL\" # Block numbers are often cardinal numbers\n",
+ "]\n",
+ "\n",
+ "print(f\"\\n✅ Extraction complete!\")\n",
+ "print(f\" Total entities: {len(all_entities)}\")\n",
+ "print(f\" Standard labels: {list(classified.keys())}\")\n",
+ "print(f\" Transactions (filtered): {len(transactions)}\")\n",
+ "print(f\" Wallets/Addresses (filtered): {len(wallets)}\")\n",
+ "print(f\" Blocks (filtered): {len(blocks)}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Extracting Transaction Relationships\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.semantic_extract import RelationExtractor\n",
+ "\n",
+ "# Use ML-based dependency parsing to avoid rate limits\n",
+ "relation_extractor = RelationExtractor(\n",
+ " method=\"dependency\", # ML/NLP method - no API calls needed\n",
+ " verbose=True\n",
+ ")\n",
+ "\n",
+ "all_relationships = []\n",
+ "error_count = 0\n",
+ "print(f\"Extracting relationships from {len(chunked_documents)} chunks using ML (dependency parsing)...\")\n",
+ "\n",
+ "for i, chunk in enumerate(chunked_documents, 1):\n",
+ " chunk_text = chunk.text if hasattr(chunk, 'text') else str(chunk)\n",
+ " try:\n",
+ " relationships = relation_extractor.extract_relations(\n",
+ " chunk_text,\n",
+ " entities=all_entities,\n",
+ " relation_types=[\"transfers\", \"from\", \"to\", \"in_block\", \"contains\", \"flows_to\"],\n",
+ " verbose=True\n",
+ " )\n",
+ " all_relationships.extend(relationships)\n",
+ " except Exception as e:\n",
+ " error_count += 1\n",
+ " if error_count <= 3:\n",
+ " print(f\" Warning: Error on chunk {i}: {str(e)[:100]}\")\n",
+ " \n",
+ " if i % 20 == 0 or i == len(chunked_documents):\n",
+ " print(f\" Processed {i}/{len(chunked_documents)} chunks ({len(all_relationships)} relationships found)\")\n",
+ "\n",
+ "if error_count > 0:\n",
+ " print(f\" Note: {error_count} chunks had errors during relation extraction\")\n",
+ "\n",
+ "print(f\"\\n✅ Extracted {len(all_relationships)} relationships\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Detecting Transaction Conflicts\n",
+ "\n",
+ "- **Multi-Type Detection**: Detects entity, relationship, and temporal conflicts across transaction network\n",
+ "- **Most Recent Strategy**: Uses `most_recent` resolution for transaction data (most accurate for blockchain)\n",
+ "- **Source-Aware Resolution**: Considers source reliability and confidence scores for conflict resolution\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.conflicts import ConflictDetector, ConflictResolver\n",
+ "\n",
+ "# Initialize conflict detection and resolution\n",
+ "conflict_detector = ConflictDetector()\n",
+ "conflict_resolver = ConflictResolver()\n",
+ "\n",
+ "# Use Semantica's conflict detection methods directly\n",
+ "# Detects entity, relationship, and temporal conflicts\n",
+ "print(f\"Detecting conflicts in {len(all_entities)} entities and {len(all_relationships)} relationships...\")\n",
+ "\n",
+ "# Convert to dict format for conflict detection (Semantica expects dicts)\n",
+ "entity_dicts = [{\"id\": e.text, \"text\": e.text, \"type\": e.label, \"confidence\": getattr(e, 'confidence', 1.0)} for e in all_entities]\n",
+ "relationship_dicts = [{\"id\": f\"{r.subject.text}_{r.predicate}_{r.object.text}\", \"source_id\": r.subject.text, \"target_id\": r.object.text, \"type\": r.predicate} for r in all_relationships]\n",
+ "\n",
+ "# Detect all conflict types using Semantica's methods\n",
+ "all_conflicts = []\n",
+ "all_conflicts.extend(conflict_detector.detect_entity_conflicts(entity_dicts))\n",
+ "all_conflicts.extend(conflict_detector.detect_relationship_conflicts(relationship_dicts))\n",
+ "all_conflicts.extend(conflict_detector.detect_temporal_conflicts(entity_dicts))\n",
+ "\n",
+ "print(f\"Detected {len(all_conflicts)} total conflicts\")\n",
+ "\n",
+ "# Resolve conflicts using best strategy for transaction networks\n",
+ "if all_conflicts:\n",
+ " print(f\"Resolving conflicts using 'most_recent' strategy (best for transaction data)...\")\n",
+ " resolved = conflict_resolver.resolve_conflicts(all_conflicts, strategy=\"most_recent\")\n",
+ " resolved_count = len([r for r in resolved if r.resolved])\n",
+ " print(f\"✅ Resolved {resolved_count}/{len(all_conflicts)} conflicts\")\n",
+ "else:\n",
+ " print(\"✅ No conflicts detected - data is consistent\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building Temporal Transaction Network Graph\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.kg import GraphBuilder\n",
+ "\n",
+ "# Conflicts already resolved - disable expensive operations\n",
+ "graph_builder = GraphBuilder(\n",
+ " merge_entities=False, # Skip entity merging (already done in conflict resolution)\n",
+ " resolve_conflicts=False, # Conflicts already resolved\n",
+ " entity_resolution_strategy=\"exact\", \n",
+ " enable_temporal=True,\n",
+ " temporal_granularity=TEMPORAL_GRANULARITY,\n",
+ " track_history=True, \n",
+ " version_snapshots=True \n",
+ ")\n",
+ "\n",
+ "# Build graph - Semantica's build() method automatically shows progress and ETA\n",
+ "kg_sources = [{\n",
+ " \"entities\": [{\"text\": e.text, \"type\": e.label, \"confidence\": getattr(e, 'confidence', 1.0)} for e in all_entities],\n",
+ " \"relationships\": [{\"source\": r.subject.text, \"target\": r.object.text, \"type\": r.predicate, \"confidence\": getattr(r, 'confidence', 1.0)} for r in all_relationships]\n",
+ "}]\n",
+ "\n",
+ "kg = graph_builder.build(kg_sources)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Generating Embeddings for Transactions and Wallets\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.embeddings import EmbeddingGenerator\n",
+ "\n",
+ "embedding_gen = EmbeddingGenerator(\n",
+ " provider=\"sentence_transformers\",\n",
+ " model=EMBEDDING_MODEL\n",
+ ")\n",
+ "\n",
+ "print(f\"Generating embeddings for {len(transactions)} transactions and {len(wallets)} wallets...\")\n",
+ "transaction_texts = [t.text for t in transactions]\n",
+ "transaction_embeddings = embedding_gen.generate_embeddings(transaction_texts)\n",
+ "\n",
+ "wallet_texts = [w.text for w in wallets]\n",
+ "wallet_embeddings = embedding_gen.generate_embeddings(wallet_texts)\n",
+ "\n",
+ "print(f\"Generated {len(transaction_embeddings)} transaction embeddings and {len(wallet_embeddings)} wallet embeddings\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Populating Vector Store\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.vector_store import VectorStore\n",
+ "\n",
+ "vector_store = VectorStore(backend=\"faiss\", dimension=EMBEDDING_DIMENSION)\n",
+ "\n",
+ "print(f\"Storing {len(transaction_embeddings)} transaction vectors and {len(wallet_embeddings)} wallet vectors...\")\n",
+ "transaction_ids = vector_store.store_vectors(\n",
+ " vectors=transaction_embeddings,\n",
+ " metadata=[{\"type\": \"transaction\", \"name\": t.text, \"label\": t.label} for t in transactions]\n",
+ ")\n",
+ "\n",
+ "wallet_ids = vector_store.store_vectors(\n",
+ " vectors=wallet_embeddings,\n",
+ " metadata=[{\"type\": \"wallet\", \"name\": w.text, \"label\": w.label} for w in wallets]\n",
+ ")\n",
+ "\n",
+ "print(f\"Stored {len(transaction_ids)} transaction vectors and {len(wallet_ids)} wallet vectors\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analyzing Graph Structure\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.kg import GraphAnalyzer, CentralityCalculator, CommunityDetector\n",
+ "\n",
+ "graph_analyzer = GraphAnalyzer()\n",
+ "centrality_calc = CentralityCalculator()\n",
+ "community_detector = CommunityDetector()\n",
+ "\n",
+ "analysis = graph_analyzer.analyze_graph(kg)\n",
+ "\n",
+ "degree_centrality = centrality_calc.calculate_degree_centrality(kg)\n",
+ "betweenness_centrality = centrality_calc.calculate_betweenness_centrality(kg)\n",
+ "closeness_centrality = centrality_calc.calculate_closeness_centrality(kg)\n",
+ "\n",
+ "communities = community_detector.detect_communities(kg, method=\"louvain\")\n",
+ "connectivity = graph_analyzer.analyze_connectivity(kg)\n",
+ "\n",
+ "print(f\"Graph analytics:\")\n",
+ "print(f\" - Communities: {len(communities)}\")\n",
+ "print(f\" - Connected components: {len(connectivity.get('components', []))}\")\n",
+ "print(f\" - Graph density: {analysis.get('density', 0):.3f}\")\n",
+ "print(f\" - Central nodes (degree): {len(degree_centrality)}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Temporal Graph Queries\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.kg import TemporalGraphQuery\n",
+ "\n",
+ "temporal_query = TemporalGraphQuery(\n",
+ " enable_temporal_reasoning=True,\n",
+ " temporal_granularity=TEMPORAL_GRANULARITY\n",
+ ")\n",
+ "\n",
+ "query_results = temporal_query.query_at_time(\n",
+ " kg,\n",
+ " query={\"type\": \"Transaction\"},\n",
+ " at_time=\"2024-01-01\"\n",
+ ")\n",
+ "\n",
+ "evolution = temporal_query.analyze_evolution(kg)\n",
+ "temporal_patterns = temporal_query.query_temporal_pattern(kg, pattern=\"sequence\")\n",
+ "\n",
+ "print(f\"Temporal queries: {query_results.get('num_relationships', 0)} relationships at query time\")\n",
+ "print(f\"Temporal patterns detected: {temporal_patterns.get('num_patterns', 0)}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Detecting Patterns and Whale Movements\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "# Detect whale movements (large transactions)\n",
+ "whale_wallets = []\n",
+ "for entity in kg.get(\"entities\", []):\n",
+ " if entity.get(\"type\") in [\"Wallet\", \"Address\"]:\n",
+ " # Check for large transaction relationships\n",
+ " related_rels = [r for r in kg.get(\"relationships\", []) \n",
+ " if r.get(\"source\") == entity.get(\"id\") or r.get(\"target\") == entity.get(\"id\")]\n",
+ " if any(\"large\" in str(r.get(\"type\", \"\")).lower() or \"whale\" in str(r.get(\"type\", \"\")).lower() \n",
+ " for r in related_rels):\n",
+ " whale_wallets.append(entity)\n",
+ "\n",
+ "# Detect suspicious patterns (high frequency transactions)\n",
+ "suspicious_patterns = []\n",
+ "for wallet in wallets[:10]:\n",
+ " wallet_name = wallet.text\n",
+ " # Find all relationships where wallet is source or target, and connected to Transaction entities\n",
+ " wallet_relationships = [\n",
+ " r for r in kg.get(\"relationships\", [])\n",
+ " if (r.get(\"source\") == wallet_name or r.get(\"target\") == wallet_name)\n",
+ " ]\n",
+ " # Find connected Transaction entities\n",
+ " transaction_ids = set()\n",
+ " for rel in wallet_relationships:\n",
+ " other_entity_id = rel.get(\"target\") if rel.get(\"source\") == wallet_name else rel.get(\"source\")\n",
+ " # Check if the other entity is a Transaction\n",
+ " other_entity = next((e for e in kg.get(\"entities\", []) if e.get(\"id\") == other_entity_id), None)\n",
+ " if other_entity and other_entity.get(\"type\") == \"Transaction\":\n",
+ " transaction_ids.add(other_entity_id)\n",
+ " \n",
+ " transaction_count = len(transaction_ids)\n",
+ " if transaction_count > 5: # High transaction frequency\n",
+ " suspicious_patterns.append({\n",
+ " 'wallet': wallet_name,\n",
+ " 'transaction_count': transaction_count\n",
+ " })\n",
+ "\n",
+ "print(f\"Whale tracking: {len(whale_wallets)} large transaction wallets identified\")\n",
+ "print(f\"Suspicious patterns: {len(suspicious_patterns)} high-frequency wallets\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analyzing Token Flows\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Analyze token flows through the network\n",
+ "from collections import deque\n",
+ "\n",
+ "flow_analysis = []\n",
+ "for transaction in transactions[:10]:\n",
+ " tx_name = transaction.text\n",
+ " \n",
+ " # Build adjacency list from relationships\n",
+ " adjacency = {}\n",
+ " for rel in kg.get(\"relationships\", []):\n",
+ " source = rel.get(\"source\")\n",
+ " target = rel.get(\"target\")\n",
+ " if source and target:\n",
+ " if source not in adjacency:\n",
+ " adjacency[source] = []\n",
+ " if target not in adjacency[source]:\n",
+ " adjacency[source].append(target)\n",
+ " if target not in adjacency:\n",
+ " adjacency[target] = []\n",
+ " if source not in adjacency[target]:\n",
+ " adjacency[target].append(source)\n",
+ " \n",
+ " # BFS to find wallets within 2 hops\n",
+ " if tx_name not in adjacency:\n",
+ " continue\n",
+ " \n",
+ " queue = deque([(tx_name, [tx_name], 0)])\n",
+ " visited = {tx_name}\n",
+ " paths_to_wallets = []\n",
+ " \n",
+ " while queue:\n",
+ " node, path, hops = queue.popleft()\n",
+ " \n",
+ " if hops > 2: # Max 2 hops\n",
+ " continue\n",
+ " \n",
+ " # Check if current node is a Wallet or Address\n",
+ " entity = next((e for e in kg.get(\"entities\", []) if e.get(\"id\") == node), None)\n",
+ " if entity and entity.get(\"type\") in [\"Wallet\", \"Address\"] and node != tx_name:\n",
+ " paths_to_wallets.append({\n",
+ " 'transaction': tx_name,\n",
+ " 'flow_path': path,\n",
+ " 'target': node,\n",
+ " 'path_length': len(path) - 1\n",
+ " })\n",
+ " \n",
+ " # Continue BFS\n",
+ " for neighbor in adjacency.get(node, []):\n",
+ " if neighbor not in visited:\n",
+ " visited.add(neighbor)\n",
+ " queue.append((neighbor, path + [neighbor], hops + 1))\n",
+ " \n",
+ " flow_analysis.extend(paths_to_wallets)\n",
+ "\n",
+ "flow_analysis.sort(key=lambda x: x['path_length'])\n",
+ "\n",
+ "print(f\"Flow analysis: {len(flow_analysis)} token flow paths identified\")\n",
+ "for i, flow in enumerate(flow_analysis[:5], 1):\n",
+ " print(f\"{i}. {flow['transaction']} -> {flow['target']} (path length: {flow['path_length']})\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Storing Transaction Network (Optional)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.graph_store import GraphStore\n",
+ "\n",
+ "# Optional: Store to persistent graph database\n",
+ "# graph_store = GraphStore(backend=\"neo4j\", uri=\"bolt://localhost:7687\", user=\"neo4j\", password=\"password\")\n",
+ "# graph_store.store_graph(kg)\n",
+ "\n",
+ "print(\"Graph store configured (commented out for demo)\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## GraphRAG: Hybrid Vector + Graph Queries\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.context import AgentContext\n",
+ "\n",
+ "context = AgentContext(vector_store=vector_store, knowledge_graph=kg)\n",
+ "\n",
+ "query = \"What are the largest transactions?\"\n",
+ "results = context.retrieve(\n",
+ " query,\n",
+ " max_results=10,\n",
+ " use_graph=True,\n",
+ " expand_graph=True,\n",
+ " include_entities=True,\n",
+ " include_relationships=True\n",
+ ")\n",
+ "\n",
+ "print(f\"GraphRAG query: '{query}'\")\n",
+ "print(f\"\\nRetrieved {len(results)} results:\\n\")\n",
+ "for i, result in enumerate(results[:5], 1):\n",
+ " print(f\"{i}. Score: {result.get('score', 0):.3f}\")\n",
+ " print(f\" Content: {result.get('content', '')[:200]}...\")\n",
+ " if result.get('related_entities'):\n",
+ " print(f\" Related entities: {len(result['related_entities'])}\")\n",
+ " print()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualizing the Transaction Network\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.visualization import KGVisualizer\n",
+ "\n",
+ "# Create visualizer with force-directed layout for better interactive visualization\n",
+ "visualizer = KGVisualizer(\n",
+ " layout=\"force\", # Force-directed layout for better node distribution\n",
+ " color_scheme=\"vibrant\", # Better color scheme\n",
+ " node_size=15,\n",
+ " edge_width=1.5\n",
+ ")\n",
+ "\n",
+ "# Create interactive network visualization\n",
+ "fig = visualizer.visualize_network(\n",
+ " kg,\n",
+ " output=\"interactive\", # Interactive Plotly visualization\n",
+ " file_path=\"transaction_network.html\", # Also save to HTML file\n",
+ " node_color_by=\"type\", # Color nodes by entity type\n",
+ " hover_data=[\"type\", \"label\"] # Show type and label in hover tooltip\n",
+ ")\n",
+ "\n",
+ "# Display the interactive figure in the notebook\n",
+ "fig.show()\n",
+ "\n",
+ "print(\"✅ Interactive visualization displayed above\")\n",
+ "print(\"📁 Visualization also saved to transaction_network.html\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Exporting Results\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from semantica.export import GraphExporter, export_csv\n",
+ "\n",
+ "# Export to graph formats using GraphExporter\n",
+ "graph_exporter = GraphExporter()\n",
+ "graph_exporter.export(kg, file_path=\"transaction_network.json\", format=\"json\")\n",
+ "graph_exporter.export(kg, file_path=\"transaction_network.graphml\", format=\"graphml\")\n",
+ "graph_exporter.export(kg, file_path=\"transaction_network.gexf\", format=\"gexf\")\n",
+ "graph_exporter.export(kg, file_path=\"transaction_network.dot\", format=\"dot\")\n",
+ "\n",
+ "# Export to CSV using export_csv convenience function\n",
+ "# This creates separate CSV files for entities and relationships\n",
+ "export_csv(kg, \"transaction_network\")\n",
+ "\n",
+ "print(\"✅ Exported transaction network to multiple formats:\")\n",
+ "print(\" - JSON: transaction_network.json\")\n",
+ "print(\" - GraphML: transaction_network.graphml\")\n",
+ "print(\" - GEXF: transaction_network.gexf\")\n",
+ "print(\" - DOT: transaction_network.dot\")\n",
+ "print(\" - CSV: transaction_network_entities.csv, transaction_network_relationships.csv\")\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/cookbook/use_cases/cybersecurity/01_Real_Time_Anomaly_Detection.ipynb b/cookbook/use_cases/cybersecurity/01_Real_Time_Anomaly_Detection.ipynb
index 0816a068..ff0bb2d8 100644
--- a/cookbook/use_cases/cybersecurity/01_Real_Time_Anomaly_Detection.ipynb
+++ b/cookbook/use_cases/cybersecurity/01_Real_Time_Anomaly_Detection.ipynb
@@ -60,32 +60,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq sentence-transformers scikit-learn\n"
]
@@ -99,7 +76,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -124,40 +101,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Ingesting from 5 feed sources...\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "🧠 Semantica - 📊 Current Progress
| Status | Action | Module | Submodule | File | Time |
|---|
| ✅ | Semantica is deduplicating | 🔄 deduplication | SimilarityCalculator | - | 0.02s |
| ✅ | Semantica is indexing | 📊 vector_store | VectorStore | - | 0.01s |
| ✅ | Semantica is reasoning | 🤔 reasoning | Reasoner | - | 0.00s |
| ✅ | Semantica is processing | 🔗 context | ContextRetriever | - | 7.41s |
| ❌ | Semantica is embedding | 💾 embeddings | TextEmbedder | - | 0.00s |
| ✅ | Semantica is processing | 🔗 context | AgentMemory | - | 0.02s |
| ✅ | Semantica is visualizing | 📈 visualization | KGVisualizer | - | 0.14s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | anomaly_detection_kg.json | 0.02s |
| ✅ | Semantica is exporting | 💾 export | GraphExporter | anomaly_detection_kg.graphml | 0.00s |
| ❌ | Semantica is exporting | 💾 export | GraphExporter | anomaly_detection_alerts.csv | 0.00s |
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " [1/5] US-CERT Alerts: 10 documents\n",
- " [2/5] SANS ISC: 10 documents\n",
- " [3/5] Krebs on Security: 10 documents\n",
- " [4/5] ThreatPost: 10 documents\n",
- "Ingested 40 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.ingest import FeedIngestor, StreamIngestor, FileIngestor\n",
"import os\n",
@@ -223,18 +169,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Parsing 40 documents...\n",
- " Parsed 40/40 documents...\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.parse import DocumentParser\n",
"\n",
@@ -266,21 +203,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Normalizing 40 documents...\n",
- " Normalized 40/40 documents...\n",
- "Chunking 40 documents...\n",
- " Chunked 40/40 documents (307 chunks so far)\n",
- "Created 307 chunks from 40 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.normalize import TextNormalizer\n",
"from semantica.split import TextSplitter\n",
@@ -329,34 +254,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting entities from 307 chunks...\n",
- " Processed 20/307 chunks (6 entities found)\n",
- " Processed 40/307 chunks (10 entities found)\n",
- " Processed 60/307 chunks (15 entities found)\n",
- " Processed 80/307 chunks (26 entities found)\n",
- " Processed 100/307 chunks (61 entities found)\n",
- " Processed 120/307 chunks (86 entities found)\n",
- " Processed 140/307 chunks (98 entities found)\n",
- " Processed 160/307 chunks (116 entities found)\n",
- " Processed 180/307 chunks (144 entities found)\n",
- " Processed 200/307 chunks (160 entities found)\n",
- " Processed 220/307 chunks (198 entities found)\n",
- " Processed 240/307 chunks (232 entities found)\n",
- " Processed 260/307 chunks (247 entities found)\n",
- " Processed 280/307 chunks (257 entities found)\n",
- " Processed 300/307 chunks (269 entities found)\n",
- " Processed 307/307 chunks (273 entities found)\n",
- "Extracted 3 IPs, 28 users, 0 alerts\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import NERExtractor\n",
"\n",
@@ -403,34 +303,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting relationships from 307 chunks using 267 filtered entities...\n",
- " Processed 20/307 chunks (534 relationships found)\n",
- " Processed 40/307 chunks (534 relationships found)\n",
- " Processed 60/307 chunks (534 relationships found)\n",
- " Processed 80/307 chunks (534 relationships found)\n",
- " Processed 100/307 chunks (534 relationships found)\n",
- " Processed 120/307 chunks (534 relationships found)\n",
- " Processed 140/307 chunks (534 relationships found)\n",
- " Processed 160/307 chunks (534 relationships found)\n",
- " Processed 180/307 chunks (534 relationships found)\n",
- " Processed 200/307 chunks (534 relationships found)\n",
- " Processed 220/307 chunks (534 relationships found)\n",
- " Processed 240/307 chunks (534 relationships found)\n",
- " Processed 260/307 chunks (534 relationships found)\n",
- " Processed 280/307 chunks (534 relationships found)\n",
- " Processed 300/307 chunks (534 relationships found)\n",
- " Processed 307/307 chunks (534 relationships found)\n",
- "Extracted 534 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import RelationExtractor\n",
"\n",
@@ -487,19 +362,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detecting conflicts in 273 entities and 534 relationships...\n",
- "Detected 0 conflicts (0 entity, 0 relationship)\n",
- "No conflicts detected\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.conflicts import ConflictDetector, ConflictResolver\n",
"\n",
@@ -570,37 +435,9 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building knowledge graph...\n",
- "Processing 273 entities, 534 relationships (807 total)...\n",
- " Entities: 100/273 (36.6%) | ETA: 0.0s | Rate: 77629.2/s\n",
- " Entities: 200/273 (73.3%) | ETA: 0.0s | Rate: 155258.3/s\n",
- " Entities: 273/273 (100.0%) | ETA: 0.0s | Rate: 211927.6/s\n",
- " Relationships: 100/534\n",
- " Relationships: 200/534\n",
- " Relationships: 300/534\n",
- " Relationships: 400/534\n",
- " Relationships: 500/534\n",
- " Relationships: 534/534\n",
- "Building graph structure...\n",
- "✅ Graph structure built (0.00s)\n",
- "\n",
- "============================================================\n",
- "✅ Knowledge Graph Build Complete\n",
- " Entities: 273\n",
- " Relationships: 534\n",
- " Total time: 0.00s\n",
- "============================================================\n",
- "Graph: 273 entities, 534 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import GraphBuilder\n",
"\n",
@@ -634,25 +471,9 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generating embeddings for 6 events and 3 IPs...\n",
- "Generated 6 event embeddings and 3 IP embeddings\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.embeddings import EmbeddingGenerator\n",
"\n",
@@ -681,25 +502,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Storing 6 event vectors and 3 IP vectors...\n",
- "Stored 6 event vectors and 3 IP vectors\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.vector_store import VectorStore\n",
"\n",
@@ -728,18 +533,9 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Temporal queries: 534 alerts at query time\n",
- "Temporal patterns detected: 0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import TemporalGraphQuery\n",
"\n",
@@ -772,16 +568,7 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Pattern detection: 0 anomaly patterns found\n",
- "Suspicious IPs: 0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import GraphAnalyzer\n",
"\n",
@@ -839,18 +626,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Inferred 0 facts\n",
- "Found 0 anomaly paths\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.reasoning import Reasoner\n",
"from semantica.kg import GraphAnalyzer\n",
@@ -895,17 +673,9 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Graph store configured (commented out for demo)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.graph_store import GraphStore\n",
"\n",
@@ -925,55 +695,9 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Embedding generation failed: Text cannot be empty or whitespace-only\n",
- "Using random fallback embedding\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "GraphRAG Query with Reasoning: 'What IPs are associated with security alerts?'\n",
- "\n",
- "================================================================================\n",
- "\n",
- "Generated Response:\n",
- "Based on the retrieved context and reasoning paths, we can infer that certain IPs are associated with security alerts due to their involvement in malicious activities.\n",
- "\n",
- "Context 1 and Context 2 both mention phishing as a related attack to malware and suspicious activity, respectively. This indicates that IPs involved in phishing attacks may trigger security alerts.\n",
- "\n",
- "Context 3 further supports this by mentioning phishing as a related attack to Suspicious Activity.\n",
- "\n",
- "The reasoning paths reveal multi-hop connections that strengthen this inference:\n",
- "\n",
- "- Path 1: phishing --[related_to]--> exploit. This suggests that IPs involved in phishing attacks may also be associated with exploit attacks, which could trigger security alerts.\n",
- "- Path 2: phishing --[related_to]--> community. Although this path does not directly relate to security alerts, it implies that IPs involved in phishing attacks may be part of a larger community or network that could be monitored for suspicious activity.\n",
- "- Path 3: phishing --[related_to]--> intrusion. This path indicates that IPs involved in phishing attacks may also be associated with intrusion attacks, which could trigger security alerts.\n",
- "\n",
- "Based on these connections, we can conclude that IPs involved in phishing attacks, exploit attacks, and intrusion attacks are likely to be associated with security alerts. However, the specific IPs are not mentioned in the context.\n",
- "\n",
- "Therefore, the answer to the question is:\n",
- "\n",
- "IPs associated with security alerts are likely those involved in phishing, exploit, and intrusion attacks, as these activities are related to malicious activities that trigger security alerts.\n",
- "\n",
- "================================================================================\n",
- "\n",
- "Reasoning Path:\n",
- "phishing --[related_to]--> exploit\n",
- "\n",
- "Confidence: 0.982\n",
- "Sources Used: 4\n",
- "Reasoning Paths Found: 497\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.context import AgentContext\n",
"from semantica.llms import Groq\n",
@@ -1015,5722 +739,9 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
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- "plotlyServerURL": "https://plot.ly"
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- "geo": {
- "bgcolor": "white",
- "lakecolor": "white",
- "landcolor": "#E5ECF6",
- "showlakes": true,
- "showland": true,
- "subunitcolor": "white"
- },
- "hoverlabel": {
- "align": "left"
- },
- "hovermode": "closest",
- "mapbox": {
- "style": "light"
- },
- "paper_bgcolor": "white",
- "plot_bgcolor": "#E5ECF6",
- "polar": {
- "angularaxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "ticks": ""
- },
- "bgcolor": "#E5ECF6",
- "radialaxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "ticks": ""
- }
- },
- "scene": {
- "xaxis": {
- "backgroundcolor": "#E5ECF6",
- "gridcolor": "white",
- "gridwidth": 2,
- "linecolor": "white",
- "showbackground": true,
- "ticks": "",
- "zerolinecolor": "white"
- },
- "yaxis": {
- "backgroundcolor": "#E5ECF6",
- "gridcolor": "white",
- "gridwidth": 2,
- "linecolor": "white",
- "showbackground": true,
- "ticks": "",
- "zerolinecolor": "white"
- },
- "zaxis": {
- "backgroundcolor": "#E5ECF6",
- "gridcolor": "white",
- "gridwidth": 2,
- "linecolor": "white",
- "showbackground": true,
- "ticks": "",
- "zerolinecolor": "white"
- }
- },
- "shapedefaults": {
- "line": {
- "color": "#2a3f5f"
- }
- },
- "ternary": {
- "aaxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "ticks": ""
- },
- "baxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "ticks": ""
- },
- "bgcolor": "#E5ECF6",
- "caxis": {
- "gridcolor": "white",
- "linecolor": "white",
- "ticks": ""
- }
- },
- "title": {
- "x": 0.05
- },
- "xaxis": {
- "automargin": true,
- "gridcolor": "white",
- "linecolor": "white",
- "ticks": "",
- "title": {
- "standoff": 15
- },
- "zerolinecolor": "white",
- "zerolinewidth": 2
- },
- "yaxis": {
- "automargin": true,
- "gridcolor": "white",
- "linecolor": "white",
- "ticks": "",
- "title": {
- "standoff": 15
- },
- "zerolinecolor": "white",
- "zerolinewidth": 2
- }
- }
- },
- "title": {
- "text": "Anomaly Detection Knowledge Graph"
- },
- "width": 1200,
- "xaxis": {
- "showgrid": false,
- "showticklabels": false,
- "zeroline": false
- },
- "yaxis": {
- "showgrid": false,
- "showticklabels": false,
- "zeroline": false
- }
- }
- }
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Interactive visualization displayed above\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.visualization import KGVisualizer\n",
"\n",
@@ -6778,18 +789,9 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Exported knowledge graph to JSON and GraphML formats\n",
- "Exported knowledge graph entities and relationships to CSV\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.export import GraphExporter, CSVExporter\n",
"\n",
diff --git a/cookbook/use_cases/cybersecurity/02_Threat_Intelligence_Hybrid_RAG.ipynb b/cookbook/use_cases/cybersecurity/02_Threat_Intelligence_Hybrid_RAG.ipynb
index 11c38b67..75fd5b24 100644
--- a/cookbook/use_cases/cybersecurity/02_Threat_Intelligence_Hybrid_RAG.ipynb
+++ b/cookbook/use_cases/cybersecurity/02_Threat_Intelligence_Hybrid_RAG.ipynb
@@ -63,32 +63,9 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Note: you may need to restart the kernel to use updated packages.\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~gno (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~lotly (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n",
- "WARNING: Ignoring invalid distribution ~ython-socketio (c:\\Users\\Mohd Kaif\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%pip install -qU semantica networkx matplotlib plotly pandas faiss-cpu beautifulsoup4 groq sentence-transformers scikit-learn\n"
]
@@ -102,7 +79,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -127,40 +104,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Ingesting from 6 feed sources...\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "🧠 Semantica - 📊 Current Progress
| Status | Action | Module | Submodule | Progress | ETA | Rate | Time |
|---|
| ❌ | Semantica is parsing | 🔍 parse | DocumentParser | - | - | - | 0.00s |
| ❌ | Semantica is parsing | 🔍 parse | DocumentParser | - | - | - | 0.00s |
| ❌ | Semantica is parsing | 🔍 parse | DocumentParser | - | - | - | 0.00s |
| ✅ | Semantica is normalizing | 🔧 normalize | TextNormalizer | 100.0% | - | - | 0.00s |
| ✅ | Semantica is extracting | 🎯 semantic_extract | NERExtractor | 100.0% | - | - | 0.70s |
| ✅ | Semantica is extracting | 🎯 semantic_extract | RelationExtractor | 100.0% | - | - | 0.47s |
| ✅ | Semantica is deduplicating | 🔄 deduplication | DuplicateDetector | 100.0% (3291/3291) | - | 516.7/s | 3.41s |
| ✅ | Semantica is deduplicating | 🔄 deduplication | SimilarityCalculator | 100.0% (26870/26870) | - | 4219.7/s | 1.83s |
| ✅ | Semantica is deduplicating | 🔄 deduplication | EntityMerger | 100.0% (111/111) | - | 17.4/s | 6.38s |
| ✅ | Semantica is deduplicating | 🔄 deduplication | MergeStrategyManager | 100.0% (4/4) | - | 144.6/s | 0.02s |
"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 [1/6] US-CERT Alerts: 10 documents\n",
- "🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 [2/6] SANS ISC: 10 documents\n",
- "🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 [3/6] Krebs on Security: 10 documents\n",
- "🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 | 🧠 Semantica is ingesting: Ingested 10 items |███████████████| 100.0% ✅📥 [4/6] ThreatPost: 10 documents\n",
- "🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.bleepingcomputer.com/feed/ ❌📥 (0.5s) | 🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.securityweek.com/rss ❌📥 (0.4s)Ingested 40 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.ingest import FeedIngestor, WebIngestor, FileIngestor\n",
"import os\n",
@@ -233,22 +179,9 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Parsing 40 documents...\n",
- "🧠 Semantica is ingesting: 403 Client Error: Forbidden for url: https://www.securityweek.com/rss ❌📥 (0.4s) | 🧠 Semantica is parsing: Document: p>\n",
- "🧠 Semantica is parsing: Document: p> \n",
- " 🔄🔍 (0.0s) | 🧠 Semantica is parsing: Document: p>\n",
- "🧠 Semantica is parsing: Document: p> \n",
- " 🔄🔍 (0.0s) | 🧠 Semantica is parsing: Document file not found: Direct navigation -- the act of visiting a website by manually typing a domain name in a web browser -- has never been riskier: A new study finds the vast majority of \"parked\" domains -- mostly expired or dormant domain names, or common misspellings of popular websites -- are now configured to redirect visitors to sites that foist scams and malware. ❌🔍 (0.0s) Parsed 40/40 documents...\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.parse import DocumentParser\n",
"\n",
@@ -280,21 +213,9 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Normalizing 40 documents...\n",
- "🧠 Semantica is parsing: Document file not found: Direct navigation -- the act of visiting a website by manually typing a domain name in a web browser -- has never been riskier: A new study finds the vast majority of \"parked\" domains -- mostly expired or dormant domain names, or common misspellings of popular websites -- are now configured to redirect visitors to sites that foist scams and malware. ❌🔍 (0.0s) | 🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 Normalized 40/40 documents...\n",
- "Chunking 40 documents...\n",
- "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 3 entities using spacy |███████████████| 100.0% ✅🎯�� Chunked 40/40 documents (85 chunks so far)\n",
- "Created 85 chunks from 40 documents\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.normalize import TextNormalizer\n",
"from semantica.split import TextSplitter\n",
@@ -341,23 +262,9 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting entities from 85 chunks using ML-based extraction...\n",
- "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 8 entities using ml |███████████████| 100.0% ✅🎯� Processed 20/85 chunks (248 entities found)\n",
- "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 2 entities using ml |███████████████| 100.0% ✅🎯� Processed 40/85 chunks (504 entities found)\n",
- "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 4 entities using ml |███████████████| 100.0% ✅🎯�� Processed 60/85 chunks (811 entities found)\n",
- "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 4 entities using ml |███████████████| 100.0% ✅🎯� Processed 80/85 chunks (851 entities found)\n",
- "🧠 Semantica is normalizing |███████████████| 100.0% ✅🔧 | 🧠 Semantica is extracting: Extracted 3 entities using ml |███████████████| 100.0% ✅🎯 Processed 85/85 chunks (857 entities found)\n",
- "Extracted 2 IOCs, 741 actors, 3 campaigns, 0 TTPs\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import NERExtractor\n",
"\n",
@@ -402,23 +309,9 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting relationships from 85 chunks using ML-based dependency parsing...\n",
- "🧠 Semantica is extracting: Extracted 3 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 11 relations using dependency |███████████████| 100.0% ✅🎯 Processed 20/85 chunks (359 relationships found)\n",
- "🧠 Semantica is extracting: Extracted 3 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 9 relations using dependency |███████████████| 100.0% ✅🎯� Processed 40/85 chunks (630 relationships found)\n",
- "🧠 Semantica is extracting: Extracted 3 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 7 relations using dependency |███████████████| 100.0% ✅🎯� Processed 60/85 chunks (830 relationships found)\n",
- "🧠 Semantica is extracting: Extracted 3 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 0 relations |███████████████| 100.0% ✅🎯█████| 100.0% ✅🎯� Processed 80/85 chunks (887 relationships found)\n",
- "🧠 Semantica is extracting: Extracted 3 entities using ml |███████████████| 100.0% ✅🎯 | 🧠 Semantica is extracting: Extracted 2 relations using dependency |███████████████| 100.0% ✅🎯 Processed 85/85 chunks (896 relationships found)\n",
- "Extracted 896 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.semantic_extract import RelationExtractor\n",
"\n",
@@ -465,21 +358,9 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Converting 857 entities to dictionaries...\n",
- "Detecting duplicates in 857 entities...\n",
- "🧠 Semantica is deduplicating: Detected 3291 duplicate candidates |███████████████| 100.0% [3291/3291] ✅🔄 (922.5/s) | 🧠 Semantica is deduplicating: Found 3291 similar pairs across 27 blocks |███████████████| 100.0% [26870/26870] ✅🔄 (12335.6/s)0/26870] ✅🔄 (12335.6/s) (12335.6/s)))Detected 111 duplicate groups\n",
- "Merging duplicates using keep_most_complete strategy...\n",
- "🧠 Semantica is deduplicating: Completed 111 merge operations |███████████████| 100.0% [111/111] ✅🔄 (17.4/s) | 🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (283.6/s)� (283.6/s)� (283.6/s) 🔄🔄 (ETA: 0.0s | 247.9/s)124.7/s)s)Deduplicated 857 entities to 414 unique entities\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.deduplication import DuplicateDetector, EntityMerger\n",
"from semantica.semantic_extract import Entity\n",
@@ -578,84 +459,9 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Detecting type conflicts in 857 entities...\n",
- "🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (283.6/s) | 🧠 Semantica is resolving: Grouping entities... 96/857 (remaining: 761) |█░░░░░░░░░░░░░░| 11.2% [96/857] 🔄⚠️ (ETA: 0.4s | 1921.4/s)"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (283.6/s) | 🧠 Semantica is resolving: Checking entity groups for type conflicts... 0/445 (remaining: 445) |░░░░░░░░░░░░░░░| 0.0% [0/445] 🔄⚠️s)))"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Type conflict detected: China Chopper conflicting types: ['PERSON', 'ORG']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (283.6/s) | 🧠 Semantica is resolving: Checking entity groups for type conflicts... 28/445 (remaining: 417) |░░░░░░░░░░░░░░░| 6.3% [28/445] 🔄⚠️ (ETA: 5.9s | 69.9/s)"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Type conflict detected: PowerShell Empire conflicting types: ['GPE', 'ORG']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (283.6/s) | 🧠 Semantica is resolving: Checking entity groups for type conflicts... 184/445 (remaining: 261) |██████░░░░░░░░░| 41.3% [184/445] 🔄⚠️ (ETA: 0.6s | 413.2/s)"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Type conflict detected: Dridex conflicting types: ['PERSON', 'ORG']\n",
- "Type conflict detected: CISA conflicting types: ['GPE', 'ORG']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (283.6/s) | 🧠 Semantica is resolving: Checking entity groups for type conflicts... 304/445 (remaining: 141) |██████████░░░░░| 68.3% [304/445] 🔄⚠️ (ETA: 0.2s | 644.9/s)"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Type conflict detected: IRGC conflicting types: ['GPE', 'ORG']\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🧠 Semantica is deduplicating: Building merged entity... (4/4, remaining: 0 steps) |███████████████| 100.0% [4/4] 🔄🔄 (283.6/s) | 🧠 Semantica is resolving: Detected 5 type conflicts |███████████████| 100.0% [436/445] ✅⚠️ (862.7/s)█████████░| 98.0% [436/445] 🔄⚠️ (ETA: 0.0s | 867.9/s)))Detected 5 type conflicts\n",
- "Resolving conflicts using highest_confidence strategy...\n",
- "🧠 Semantica is resolving: Detected 5 type conflicts |███████████████| 100.0% [436/445] ✅⚠️ (862.7/s) | 🧠 Semantica is resolving: Resolving conflicts... 0/5 (remaining: 5) |░░░░░░░░░░░░░░░| 0.0% [0/5] 🔄⚠️Resolved 5 conflicts\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.conflicts import ConflictDetector, ConflictResolver\n",
"\n",
@@ -700,27 +506,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Building knowledge graph...\n",
- "🧠 Semantica is resolving: Resolving conflicts... 0/5 (remaining: 5) |░░░░░░░░░░░░░░░| 0.0% [0/5] 🔄⚠️ | 🧠 Semantica is building: Processing relationships... 896/896 |███████████████| 100.0% [896/896] 🔄🧠 (18605.5/s) 17715.1/s)Building graph structure...\n",
- "✅ Graph structure built (0.00s)\n",
- "\n",
- "============================================================\n",
- "✅ Knowledge Graph Build Complete\n",
- " Entities: 414\n",
- " Relationships: 896\n",
- " Total time: 0.08s\n",
- "============================================================\n",
- "Graph: 414 entities, 896 relationships\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import GraphBuilder\n",
"\n",
@@ -754,24 +542,9 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Generated 2 IOC embeddings and 741 actor embeddings\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.embeddings import EmbeddingGenerator\n",
"\n",
@@ -798,25 +571,9 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "fastembed not available. Install with: pip install fastembed. Using fallback embedding method.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Storing 2 IOC vectors and 741 actor vectors...\n",
- "🧠 Semantica is building: Processing relationships... 896/896 |███████████████| 100.0% [896/896] 🔄🧠 (18605.5/s) | 🧠 Semantica is indexing: Storing 2 vectors 🔄📊 (0.0s)Stored 2 IOC vectors and 741 actor vectors\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.vector_store import VectorStore\n",
"\n",
@@ -845,18 +602,9 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Temporal queries: 6 campaigns at query time\n",
- "Temporal patterns detected: 0\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import TemporalGraphQuery\n",
"\n",
@@ -887,21 +635,9 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "🧠 Semantica is building: Calculating degree centrality 🔄🧠 (0.0s) | 🧠 Semantica is building: Detecting communities with NetworkX... 🔄🧠 (0.0s)🧠Graph analytics:\n",
- " - Communities: 4\n",
- " - Connected components: 11\n",
- " - Graph density: 0.000\n",
- " - Central nodes (degree): 4\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from semantica.kg import GraphAnalyzer, CentralityCalculator, CommunityDetector\n",
"\n",
@@ -933,87 +669,9 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Exploring knowledge graph for threat actors and entities...\n",
- "\n",
- "Found 2 threat-related entities:\n",
- " - APT (type: ORG)\n",
- " - APT TA423 (type: ORG)\n",
- "\n",
- "Searching for APT28/Fancy Bear: Not found in knowledge graph\n",
- "\n",
- "================================================================================\n",
- "Query: What are the main cybersecurity threats and threat actors mentioned?\n",
- "================================================================================\n",
- "\n",
- "🧠 Semantica is embedding: Generating text embedding: observe Credential Guard. antica is processing: Searching vector store... 🔄🔗 (0.0s)0s)0.0s)\n",
- "🧠 Semantica is embedding: Generating text embedding: href=\"https://learn.microsoft.com is a ORG.... 🔄💾 (0.0s) | 🧠 Semantica is processing: Searching vector store... 🔄🔗 (0.0s) (0.0s)"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Embedding generation failed: Text cannot be empty or whitespace-only\n",
- "Using random fallback embedding\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "================================================================================\n",
- "Generated Answer (with Multi-hop Reasoning):\n",
- "================================================================================\n",
- "Based on the retrieved context and reasoning paths, the main cybersecurity threats and threat actors mentioned are:\n",
- "\n",
- "1. **Malware**: Mentioned in the context of \"Guide to Malware Incident Prevention and Handling for Desktops\" (Context 2) and \"Securing Active\" (Reasoning Paths 1-2). This suggests that malware is a significant cybersecurity threat.\n",
- "2. **Cyber attacks**: Implied by the involvement of the Cybersecurity and Infrastructure Security Agency (Context 2) and the National Cybersecurity and Communications Integration Center (Context 4).\n",
- "3. **Phishing**: Suggested by the mention of \"redirection\" (Context 4) and the involvement of the Department of the Treasury's Financial Crimes Enforcement Network (Context 4).\n",
- "4. **Social engineering**: Implied by the mention of Microsoft Office (Context 4) and the involvement of the National Cybersecurity and Communications Integration Center (Context 4).\n",
- "\n",
- "As for threat actors, the following are mentioned:\n",
- "\n",
- "1. **Quasar**: A person (Context 1) who is not explicitly linked to any specific threat or organization.\n",
- "2. **Operation for Community Development and Empowerment**: An organization (Context 2) that is infected by the Cybersecurity and Infrastructure Security Agency (Context 2).\n",
- "3. **The United Nations**: An organization (Context 5) that provides Message Server (Context 5).\n",
- "\n",
- "Multi-hop connections:\n",
- "\n",
- "* The connection between Special Publication and Securing Active (Reasoning Paths 1-2) implies that Special Publication is a resource used to secure active systems.\n",
- "* The connection between Special Publication and Guide to Malware Incident Prevention and Handling for Desktops (Reasoning Path 3) implies that Special Publication is a resource that can help prevent and handle malware incidents.\n",
- "\n",
- "Note that the information provided is based on the retrieved context and reasoning paths, and may not be comprehensive or up-to-date.\n",
- "\n",
- "================================================================================\n",
- "\n",
- "Reasoning Details:\n",
- "- Confidence: 0.637\n",
- "- Sources: 20\n",
- "- Reasoning Paths: 638\n",
- "- Total entities in graph: 414\n",
- "- Total relationships in graph: 896\n",
- "\n",
- "Top Sources:\n",
- " 1. Score: 0.718\n",
- " Quasar is a PERSON....\n",
- " 2. Score: 0.685\n",
- " Operation for Community Development and Empowerment is a ORG. detect Special Publication (ORG). is infected by the Cybersecurity and Infrastructure Security Agency (ORG). represent Transport Layer Sec...\n",
- " 3. Score: 0.685\n",
- " SAP is a ORG....\n",
- " 4. Score: 0.649\n",
- " redirection.\n",
- "\n",
- "\n",
- " \n",
- "\n",
- "