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Enhance Temporal Knowledge Graph notebook with deep dive into modules and advanced visualization
This commit is contained in:
KaifAhmad1
@@ -6,35 +6,29 @@
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"source": [
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"[](https://colab.research.google.com/github/Hawksight-AI/semantica/blob/main/cookbook/advanced/10_Temporal_Knowledge_Graphs.ipynb)\n",
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"\n",
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"# Temporal Knowledge Graphs\n",
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"# Deep Dive: Temporal Knowledge Graphs\n",
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"\n",
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"## Overview\n",
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"\n",
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"This notebook demonstrates advanced temporal knowledge graph capabilities using TemporalGraphQuery, TemporalPatternDetector, TemporalVersionManager, and TemporalVisualizer.\n",
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"This notebook provides a comprehensive deep dive into **Temporal Knowledge Graphs (TKGs)** using Semantica. Unlike static KGs, TKGs capture the evolution of facts, relationships, and entities over time. This capability is crucial for applications like:\n",
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"\n",
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"- **Corporate History Analysis**: Tracking mergers, acquisitions, and leadership changes.\n",
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"- **Supply Chain Monitoring**: Tracing product movement and status changes.\n",
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"- **Financial Fraud Detection**: Analyzing sequences of transactions.\n",
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"\n",
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"We will build a rich scenario modeling the history of a tech ecosystem, covering 40 years of evolution.\n",
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"\n",
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"### Key Components Covered\n",
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"\n",
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"1. **`GraphBuilder` (Temporal Mode)**: Constructing KGs with time-aware properties.\n",
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"2. **`TemporalGraphQuery`**: Performing point-in-time, interval, and path queries.\n",
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"3. **`TemporalPatternDetector`**: Identifying sequences and cyclic patterns.\n",
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"4. **`TemporalVersionManager`**: Managing snapshots and comparing graph states.\n",
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"5. **`TemporalVisualizer`**: Interactive timelines and evolution plots.\n",
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"\n",
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"**Documentation**: [API Reference](https://semantica.readthedocs.io/reference/kg/)\n",
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"\n",
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"### Learning Objectives\n",
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"\n",
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"- Use TemporalGraphQuery for time-aware queries\n",
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"- Use TemporalPatternDetector to detect temporal patterns\n",
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"- Use TemporalVersionManager for temporal versioning and snapshots\n",
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"- Use TemporalVisualizer to visualize temporal data\n",
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"\n",
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"## Installation\n",
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"\n",
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"Install Semantica from PyPI:\n",
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"\n",
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"```bash\n",
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"pip install semantica\n",
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"# Or with all optional dependencies:\n",
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"pip install semantica[all]\n",
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"```\n",
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"\n",
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"---\n",
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"\n",
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"## Workflow: Build Temporal KG \u2192 Time-Aware Queries \u2192 Pattern Detection \u2192 Version Management \u2192 Visualization\n"
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"## Installation\n"
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]
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},
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{
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@@ -43,7 +37,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install semantica\n"
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"!pip install semantica[all]"
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]
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},
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{
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@@ -52,33 +46,112 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"from datetime import datetime\n",
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"from semantica.kg import GraphBuilder, TemporalGraphQuery, TemporalPatternDetector, TemporalVersionManager\n",
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"from semantica.visualization import TemporalVisualizer\n",
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"from datetime import datetime\n",
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"\n",
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"builder = GraphBuilder()\n",
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"# Ensure consistent output for reproducibility\n",
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"import random\n",
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"random.seed(42)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 1: Scenario Definition & Data Preparation\n",
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"\n",
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"We define a dataset representing the history of \"TechCorp\" and \"InnovateInc\", including their founders, products, and eventual merger.\n",
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"\n",
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"**Temporal Properties**:\n",
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"- Entities have `founded`, `born`, `released` dates.\n",
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"- Relationships have `timestamp` (point event) or `valid_from`/`valid_to` (intervals).\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 1. Define Entities with Temporal Metadata\n",
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"entities = [\n",
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" {\"id\": \"e1\", \"type\": \"Organization\", \"name\": \"Apple Inc.\", \"properties\": {\"founded\": \"1976\"}},\n",
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" {\"id\": \"e2\", \"type\": \"Person\", \"name\": \"Steve Jobs\", \"properties\": {\"born\": \"1955\"}}\n",
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" # Organizations\n",
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" {\"id\": \"org_1\", \"type\": \"Organization\", \"name\": \"TechCorp\", \"properties\": {\"founded\": \"1980-01-01\", \"industry\": \"Hardware\"}},\n",
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" {\"id\": \"org_2\", \"type\": \"Organization\", \"name\": \"InnovateInc\", \"properties\": {\"founded\": \"1995-06-15\", \"industry\": \"Software\"}},\n",
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" {\"id\": \"org_3\", \"type\": \"Organization\", \"name\": \"FutureSystems\", \"properties\": {\"founded\": \"2010-03-10\", \"industry\": \"AI\"}},\n",
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" \n",
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" # People\n",
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" {\"id\": \"per_1\", \"type\": \"Person\", \"name\": \"Alice Founder\", \"properties\": {\"born\": \"1955-05-20\"}},\n",
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" {\"id\": \"per_2\", \"type\": \"Person\", \"name\": \"Bob Coder\", \"properties\": {\"born\": \"1970-08-12\"}},\n",
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" {\"id\": \"per_3\", \"type\": \"Person\", \"name\": \"Charlie CEO\", \"properties\": {\"born\": \"1980-02-28\"}},\n",
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" \n",
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" # Products\n",
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" {\"id\": \"prod_1\", \"type\": \"Product\", \"name\": \"HomePC\", \"properties\": {\"released\": \"1985-11-20\"}},\n",
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" {\"id\": \"prod_2\", \"type\": \"Product\", \"name\": \"SoftOS\", \"properties\": {\"released\": \"1998-07-25\"}},\n",
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" {\"id\": \"prod_3\", \"type\": \"Product\", \"name\": \"SmartAI\", \"properties\": {\"released\": \"2015-01-10\"}}\n",
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"]\n",
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"\n",
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"# 2. Define Temporal Relationships\n",
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"relationships = [\n",
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" {\"source\": \"e2\", \"target\": \"e1\", \"type\": \"founded\", \"properties\": {\"timestamp\": \"1976-04-01\"}}\n",
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" # Founding Events (Point in time)\n",
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" {\"source\": \"per_1\", \"target\": \"org_1\", \"type\": \"founded\", \"timestamp\": \"1980-01-01\", \"properties\": {\"timestamp\": \"1980-01-01\"}},\n",
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" {\"source\": \"per_2\", \"target\": \"org_2\", \"type\": \"founded\", \"timestamp\": \"1995-06-15\", \"properties\": {\"timestamp\": \"1995-06-15\"}},\n",
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" \n",
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" # Employment (Intervals)\n",
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" {\"source\": \"per_1\", \"target\": \"org_1\", \"type\": \"ceo_of\", \"valid_from\": \"1980-01-01\", \"valid_to\": \"2000-01-01\", \"properties\": {\"role\": \"CEO\"}},\n",
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" {\"source\": \"per_3\", \"target\": \"org_1\", \"type\": \"ceo_of\", \"valid_from\": \"2000-01-02\", \"valid_to\": \"2023-01-01\", \"properties\": {\"role\": \"CEO\"}},\n",
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" {\"source\": \"per_2\", \"target\": \"org_2\", \"type\": \"cto_of\", \"valid_from\": \"1995-06-15\", \"valid_to\": \"2010-05-01\", \"properties\": {\"role\": \"CTO\"}},\n",
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" \n",
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" # Product Launches\n",
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" {\"source\": \"org_1\", \"target\": \"prod_1\", \"type\": \"launched\", \"timestamp\": \"1985-11-20\", \"properties\": {\"timestamp\": \"1985-11-20\"}},\n",
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" {\"source\": \"org_2\", \"target\": \"prod_2\", \"type\": \"launched\", \"timestamp\": \"1998-07-25\", \"properties\": {\"timestamp\": \"1998-07-25\"}},\n",
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" {\"source\": \"org_3\", \"target\": \"prod_3\", \"type\": \"launched\", \"timestamp\": \"2015-01-10\", \"properties\": {\"timestamp\": \"2015-01-10\"}},\n",
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" \n",
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" # Corporate Actions\n",
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" {\"source\": \"org_1\", \"target\": \"org_2\", \"type\": \"acquired\", \"timestamp\": \"2010-05-01\", \"properties\": {\"amount\": \"$5B\", \"timestamp\": \"2010-05-01\"}},\n",
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" {\"source\": \"org_1\", \"target\": \"org_3\", \"type\": \"invested_in\", \"timestamp\": \"2012-08-15\", \"properties\": {\"amount\": \"$100M\", \"timestamp\": \"2012-08-15\"}}\n",
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"]\n",
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"\n",
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"print(f\"Defined {len(entities)} entities and {len(relationships)} temporal relationships.\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 2: Building the Temporal Graph\n",
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"\n",
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"We use `GraphBuilder` with `enable_temporal=True`. This instructs the builder to index temporal properties like `timestamp`, `valid_from`, and `valid_to`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"builder = GraphBuilder(\n",
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" enable_temporal=True,\n",
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" temporal_granularity=\"day\" # Can be 'year', 'month', 'day', 'hour'\n",
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")\n",
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"\n",
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"temporal_kg = builder.build(entities, relationships)\n",
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"\n",
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"print(f\"Built temporal knowledge graph with {len(entities)} entities\")\n"
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"# The graph object now contains temporal indices\n",
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"print(\"Graph built successfully.\")\n",
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"print(f\"Nodes: {len(temporal_kg['entities'])}\")\n",
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"print(f\"Edges: {len(temporal_kg['relationships'])}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 2: Time-Aware Queries\n",
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"## Step 3: Advanced Temporal Querying\n",
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"\n",
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"Query the graph at specific time points.\n"
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"We use `TemporalGraphQuery` to ask time-sensitive questions."
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]
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},
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{
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@@ -87,25 +160,48 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"temporal_query = TemporalGraphQuery()\n",
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"query_engine = TemporalGraphQuery()\n",
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"\n",
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"query_result = temporal_query.query_time_range(\n",
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"# 1. Point-in-Time Query\n",
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"# \"Who was the CEO of TechCorp in 1990?\"\n",
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"ceo_1990 = query_engine.query_at_time(\n",
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" temporal_kg,\n",
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" query=\"Find the CEO of TechCorp\",\n",
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" timestamp=\"1990-06-01\"\n",
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")\n",
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"print(\"CEO in 1990:\", [e['id'] for e in ceo_1990.get('entities', [])])\n",
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"\n",
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"# \"Who was the CEO of TechCorp in 2015?\"\n",
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"ceo_2015 = query_engine.query_at_time(\n",
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" temporal_kg,\n",
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" query=\"Find the CEO of TechCorp\",\n",
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" timestamp=\"2015-06-01\"\n",
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")\n",
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"print(\"CEO in 2015:\", [e['id'] for e in ceo_2015.get('entities', [])])\n",
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"\n",
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"# 2. Temporal Path Finding\n",
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"# \"How did Alice (Founder) connect to SmartAI (Product released in 2015)?\"\n",
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"# This requires traversing through time: Alice -> founded TechCorp -> invested in FutureSystems -> launched SmartAI\n",
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"paths = query_engine.find_temporal_paths(\n",
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" graph=temporal_kg,\n",
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" query=\"Find entities founded in 1976\",\n",
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" start_time=\"1976-01-01\",\n",
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" end_time=\"1976-12-31\"\n",
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" source=\"per_1\", # Alice\n",
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" target=\"prod_3\", # SmartAI\n",
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" start_time=\"1980-01-01\",\n",
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" end_time=\"2020-01-01\"\n",
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")\n",
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"\n",
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"print(f\"Time-aware query returned {len(query_result.get('entities', []))} entities\")\n"
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"print(f\"\\nFound {len(paths)} temporal paths from Alice to SmartAI.\")\n",
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"for i, path in enumerate(paths):\n",
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" print(f\"Path {i+1}: {path}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 3: Temporal Pattern Detection\n",
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"## Step 4: Graph Evolution Analysis\n",
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"\n",
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"Detect temporal patterns in the graph.\n"
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"We can analyze how the graph properties change over time using `analyze_evolution`."
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]
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},
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{
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@@ -114,24 +210,56 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"pattern_detector = TemporalPatternDetector()\n",
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"evolution_stats = query_engine.analyze_evolution(\n",
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" temporal_kg,\n",
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" start_time=\"1980-01-01\",\n",
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" end_time=\"2025-01-01\",\n",
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" metrics=[\"count\", \"diversity\", \"stability\"]\n",
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")\n",
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"\n",
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"patterns = pattern_detector.detect_temporal_patterns(\n",
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"print(\"\\nEvolution Statistics (1980-2025):\")\n",
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"print(f\"Total Relationships: {evolution_stats.get('count', 'N/A')}\")\n",
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"print(f\"Relationship Diversity: {evolution_stats.get('diversity', 'N/A')}\")\n",
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"print(f\"Graph Stability: {evolution_stats.get('stability', 'N/A')}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 5: Temporal Pattern Detection\n",
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"\n",
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"We use `TemporalPatternDetector` to automatically find recurring structures, such as sequences (A -> B -> C) or cycles."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"detector = TemporalPatternDetector()\n",
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"\n",
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"# Detect sequential patterns (e.g., Founded -> Launched -> Acquired)\n",
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"sequences = detector.detect_temporal_patterns(\n",
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" temporal_kg,\n",
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" pattern_type=\"sequence\",\n",
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" min_frequency=1\n",
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")\n",
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"\n",
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"print(f\"Detected {len(patterns)} temporal patterns\")\n"
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"print(f\"\\nDetected {len(sequences)} sequential patterns.\")\n",
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"for seq in sequences[:3]: # Show top 3\n",
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" print(f\"Pattern: {seq.get('pattern')}\")\n",
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" print(f\"Support: {seq.get('support')}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 4: Version Management\n",
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"## Step 6: Version Management & Comparisons\n",
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"\n",
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"Manage temporal versions and snapshots.\n"
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"In real-world scenarios, KGs are updated in batches. `TemporalVersionManager` handles these versions."
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]
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},
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{
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@@ -142,19 +270,25 @@
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"source": [
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"version_manager = TemporalVersionManager()\n",
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"\n",
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"snapshot = version_manager.create_snapshot(temporal_kg, timestamp=datetime.now())\n",
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"# Create explicit versions\n",
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"v1_1990 = version_manager.create_version(temporal_kg, timestamp=\"1990-01-01\", version_label=\"v1.0 (Early Days)\")\n",
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"v2_2010 = version_manager.create_version(temporal_kg, timestamp=\"2010-01-01\", version_label=\"v2.0 (Post-Merger)\")\n",
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"\n",
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"print(f\"Created temporal snapshot at {snapshot.get('timestamp', 'N/A')}\")\n",
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"print(f\"Snapshot contains {len(snapshot.get('entities', []))} entities\")\n"
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"# Compare versions\n",
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"diff = version_manager.compare_versions(v1_1990, v2_2010)\n",
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"\n",
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"print(f\"\\nComparing {v1_1990['label']} vs {v2_2010['label']}:\")\n",
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"print(f\"New Entities: {diff.get('added_entities_count', 0)}\")\n",
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"print(f\"New Relationships: {diff.get('added_relationships_count', 0)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 5: Temporal Visualization\n",
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"## Step 7: Visualizing the Timeline\n",
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"\n",
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"Visualize temporal data.\n"
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"Finally, `TemporalVisualizer` brings the data to life. We will create an interactive timeline and a snapshot comparison."
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]
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},
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{
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@@ -163,9 +297,46 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"temporal_visualizer = TemporalVisualizer()\n",
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"visualizer = TemporalVisualizer()\n",
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"\n",
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"visualization = temporal_visualizer.visualize_timeline(temporal_kg, output=\"interactive\")\n"
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"# 1. Interactive Timeline\n",
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"# Prepare events for visualization (extract from KG)\n",
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"def extract_events(graph):\n",
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" events = []\n",
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" for rel in graph['relationships']:\n",
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" # Point events\n",
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" if rel.get('timestamp'):\n",
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" events.append({\n",
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" 'timestamp': rel['timestamp'],\n",
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" 'type': rel['type'],\n",
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" 'label': f\"{rel['source']} -> {rel['target']}\",\n",
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" 'entity': rel['source']\n",
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" })\n",
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" # Interval events (start)\n",
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" if rel.get('valid_from'):\n",
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" events.append({\n",
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" 'timestamp': rel['valid_from'],\n",
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" 'type': f\"{rel['type']} (start)\",\n",
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" 'label': f\"{rel['source']} -> {rel['target']}\",\n",
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" 'entity': rel['source']\n",
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" })\n",
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" return {'events': events}\n",
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"\n",
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"temporal_data = extract_events(temporal_kg)\n",
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"timeline_fig = visualizer.visualize_timeline(temporal_data, output=\"interactive\")\n",
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"# In a notebook, this would render a Plotly figure. \n",
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"# timeline_fig.show()\n",
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"\n",
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"# 2. Version History Visualization\n",
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"history = [\n",
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" {\"version\": \"v1.0\", \"timestamp\": \"1990-01-01\", \"changes\": \"Founding Era\"},\n",
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" {\"version\": \"v2.0\", \"timestamp\": \"2010-01-01\", \"changes\": \"Expansion Era\"},\n",
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" {\"version\": \"v3.0\", \"timestamp\": \"2020-01-01\", \"changes\": \"AI Era\"}\n",
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"]\n",
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"history_fig = visualizer.visualize_version_history(history, output=\"interactive\")\n",
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||||
"# history_fig.show()\n",
|
||||
"\n",
|
||||
"print(\"Visualizations generated (render requires Jupyter environment).\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -174,66 +345,38 @@
|
||||
"source": [
|
||||
"## Summary\n",
|
||||
"\n",
|
||||
"You've learned advanced temporal knowledge graph capabilities:\n",
|
||||
"In this deep dive, we:\n",
|
||||
"1. **modeled** a complex corporate history with temporal metadata.\n",
|
||||
"2. **Built** a time-aware knowledge graph using `GraphBuilder`.\n",
|
||||
"3. **Queried** specific time slices and intervals to reconstruct history.\n",
|
||||
"4. **Traced** temporal paths to understand indirect connections.\n",
|
||||
"5. **Analyzed** the graph's evolution metrics.\n",
|
||||
"6. **Managed** versions and visualized the timeline.\n",
|
||||
"7. **Visualized** the data with `TemporalVisualizer`.\n",
|
||||
"\n",
|
||||
"- **TemporalGraphQuery**: Time-aware graph querying\n",
|
||||
"- **TemporalPatternDetector**: Temporal pattern detection\n",
|
||||
"- **TemporalVersionManager**: Temporal versioning and snapshots\n",
|
||||
"- **TemporalVisualizer**: Temporal data visualization\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Snapshot Comparison and Version History\n",
|
||||
"\n",
|
||||
"Compare graph snapshots across time and visualize version history."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Create multiple versions\n",
|
||||
"version_manager = TemporalVersionManager()\n",
|
||||
"version_2020 = version_manager.create_version(temporal_kg, timestamp=\"2020-01-01\", version_label=\"v2020\")\n",
|
||||
"# Simulate changes for 2023\n",
|
||||
"temporal_kg_updated = {\n",
|
||||
" \"entities\": temporal_kg.get(\"entities\", []),\n",
|
||||
" \"relationships\": temporal_kg.get(\"relationships\", []) + [\n",
|
||||
" {\"source\": \"e1\", \"target\": \"e2\", \"type\": \"collaborated_with\", \"valid_from\": \"2023-01-01\"}\n",
|
||||
" ]\n",
|
||||
"}\n",
|
||||
"version_2023 = version_manager.create_version(temporal_kg_updated, timestamp=\"2023-01-01\", version_label=\"v2023\")\n",
|
||||
"\n",
|
||||
"# Build snapshots dict for comparison\n",
|
||||
"snapshots = {\n",
|
||||
" version_2020[\"timestamp\"]: version_2020,\n",
|
||||
" version_2023[\"timestamp\"]: version_2023\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"# Visualize snapshot comparison\n",
|
||||
"fig_snapshots = temporal_visualizer.visualize_snapshot_comparison(snapshots, output=\"interactive\")\n",
|
||||
"\n",
|
||||
"# Build version history list\n",
|
||||
"version_history = [\n",
|
||||
" {\"version\": version_2020.get(\"label\", \"v2020\"), \"timestamp\": version_2020.get(\"timestamp\"), \"changes\": f\"Entities: {len(version_2020.get('entities', []))}, Relationships: {len(version_2020.get('relationships', []))}\"},\n",
|
||||
" {\"version\": version_2023.get(\"label\", \"v2023\"), \"timestamp\": version_2023.get(\"timestamp\"), \"changes\": f\"Entities: {len(version_2023.get('entities', []))}, Relationships: {len(version_2023.get('relationships', []))}\"}\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Visualize version history\n",
|
||||
"fig_versions = temporal_visualizer.visualize_version_history(version_history, output=\"interactive\")\n"
|
||||
"This workflow forms the backbone of temporal intelligence applications in Semantica."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
"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
|
||||
}
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user