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Core Concepts
Understand the fundamental concepts behind Semantica. This guide covers the theoretical foundations, key components, and best practices for building semantic applications.
!!! info "About This Guide" This guide provides a comprehensive overview of the core concepts in Semantica. Each concept includes definitions, visual diagrams, practical examples, and guidance on when to use them.
[TOC]
Core Concepts
1. Knowledge Graphs
!!! abstract "Definition" A knowledge graph is a structured representation of entities (nodes) and their relationships (edges) with properties and attributes. It transforms unstructured data into a queryable, interconnected knowledge base.
-
Nodes (Entities)
Represent real-world objects, concepts, or events. Examples: People, Organizations, Locations, Concepts
-
Edges (Relationships)
Represent connections between entities. Examples:
works_for,located_in,founded_by,causes -
Properties
Attributes of entities and relationships. Examples: Name, Date, Confidence Score, Source
-
Metadata
Additional information about the data. Examples: Source documents, timestamps, extraction methods
Visual Example:
graph LR
A[Apple Inc.<br/>Organization<br/>Founded: 1976] -->|founded_by| B[Steve Jobs<br/>Person<br/>1955-2011]
A -->|located_in| C[Cupertino<br/>Location<br/>City]
C -->|in_state| D[California<br/>Location<br/>State]
A -->|has_ceo| E[Tim Cook<br/>Person<br/>CEO since 2011]
style A fill:#e3f2fd,stroke:#1565c0,stroke-width:2px
style B fill:#fff3e0,stroke:#ef6c00,stroke-width:2px
style C fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
style D fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
style E fill:#fff3e0,stroke:#ef6c00,stroke-width:2px
Practical Examples:
=== "Basic Usage" Build a knowledge graph from a document with just a few lines:
```python
from semantica import Semantica
# Initialize Semantica with default settings
semantica = Semantica()
# Build knowledge graph from a PDF document
result = semantica.build_knowledge_base(
sources=["company_report.pdf"],
embeddings=True, # Generate vector embeddings
graph=True # Build knowledge graph
)
kg = result["knowledge_graph"]
print(f"Extracted {len(kg['entities'])} entities")
print(f"Found {len(kg['relationships'])} relationships")
for entity in kg['entities'][:5]:
print(f" {entity['text']} ({entity['type']})")
```
=== "Direct KG Module Usage"
Use the kg module directly for more control:
```python
from semantica.kg import GraphBuilder
# Option 1: Using the convenience function (semantica.kg.build)
kg = build(
sources=[
{
"entities": [
{"id": "e1", "text": "Apple Inc.", "type": "Organization"},
{"id": "e2", "text": "Tim Cook", "type": "Person"},
{"id": "e3", "text": "Cupertino", "type": "Location"}
],
"relationships": [
{"source": "e2", "target": "e1", "type": "CEO_OF"},
{"source": "e1", "target": "e3", "type": "LOCATED_IN"}
]
}
],
merge_entities=True,
resolve_conflicts=True
)
print(f"Graph built: {kg['metadata']['num_entities']} entities, "
f"{kg['metadata']['num_relationships']} relationships")
# Option 2: Using GraphBuilder for advanced configuration
builder = GraphBuilder(
merge_entities=True,
entity_resolution_strategy="fuzzy", # "exact", "fuzzy", or "semantic"
resolve_conflicts=True,
enable_temporal=True, # Enable time-aware edges
temporal_granularity="day"
)
kg = builder.build(sources)
```
=== "Multi-Source Integration" Merge knowledge from multiple data sources:
```python
from semantica import Semantica
semantica = Semantica()
# Build graphs from different sources
kg_news = semantica.build_knowledge_base(
sources=["news_articles/"],
normalize=True
)["knowledge_graph"]
kg_reports = semantica.build_knowledge_base(
sources=["financial_reports/"],
normalize=True
)["knowledge_graph"]
# Merge into a unified knowledge graph
from semantica.kg import GraphBuilder
builder = GraphBuilder(
merge_entities=True,
entity_resolution_strategy="semantic" # Use embeddings for matching
)
unified_kg = builder.merge([kg_news, kg_reports])
print(f"Merged graph: {len(unified_kg['entities'])} unique entities")
print(f"Deduplicated from {len(kg_news['entities']) + len(kg_reports['entities'])} total")
```
=== "Graph Analytics" Analyze the structure and properties of your knowledge graph:
```python
from semantica.kg import (
GraphAnalyzer,
CentralityCalculator,
CommunityDetector,
ConnectivityAnalyzer,
analyze_graph,
calculate_centrality,
detect_communities,
analyze_connectivity
)
# Method 1: Using convenience functions
analysis = analyze_graph(kg, method="default")
centrality_scores = calculate_centrality(kg, method="degree")
communities = detect_communities(kg, method="louvain")
connectivity = analyze_connectivity(kg, method="default")
# Method 2: Using classes for more control
analyzer = GraphAnalyzer()
centrality_calc = CentralityCalculator()
community_detector = CommunityDetector()
connectivity_analyzer = ConnectivityAnalyzer()
# Run comprehensive analysis
analysis = analyzer.analyze(kg)
print(f"Graph density: {analysis['density']:.3f}")
print(f"Average degree: {analysis['avg_degree']:.2f}")
# Calculate centrality measures
degree_centrality = centrality_calc.calculate(kg, method="degree")
betweenness = centrality_calc.calculate(kg, method="betweenness")
print(f"Top entities by degree: {list(degree_centrality.items())[:3]}")
# Detect communities
communities = community_detector.detect(kg, method="louvain")
print(f"Found {len(communities)} communities")
# Analyze connectivity
conn = connectivity_analyzer.analyze(kg)
print(f"Connected components: {conn['num_components']}")
print(f"Largest component: {conn['largest_component_size']} nodes")
```
Related Modules:
kgModule - Knowledge graph construction and managementgraph_storeModule - Persistent graph storagevisualizationModule - Graph visualization
2. Entity Extraction (NER)
!!! abstract "Definition" Named Entity Recognition (NER) is the process of identifying and classifying named entities in text into predefined categories such as persons, organizations, locations, dates, and more.
Entity Types:
| Entity Type | Description | Example |
|---|---|---|
| Person | Names of people | Steve Jobs, Elon Musk, Marie Curie |
| Organization | Companies, institutions | Apple Inc., NASA, MIT |
| Location | Places, geographic entities | Cupertino, Mars, Pacific Ocean |
| Date/Time | Temporal expressions | 1976, next Monday, Q1 2024 |
| Money | Monetary values | $100 million, €50,000 |
| Event | Events and occurrences | WWDC 2024, World War II |
| Product | Products and services | iPhone 15, Tesla Model S |
| Technology | Technologies and methods | Machine Learning, Python |
!!! tip "Custom Entities"
Semantica allows you to define custom entity types via the Ontology module. You aren't limited to the standard set!
Extraction Methods:
=== "Quick Start" Extract entities from text using the convenience function:
```python
from semantica.semantic_extract import NamedEntityRecognizer
text = """
Apple Inc. was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne
in Cupertino, California on April 1, 1976. The company is now worth
over $3 trillion and employs more than 160,000 people worldwide.
"""
# Initialize the NER extractor
ner = NamedEntityRecognizer()
# Extract entities
entities = ner.extract_entities(text)
print(f"Extracted {len(entities)} entities:")
for entity in entities:
print(f" {entity['text']:20} | Type: {entity['type']:15} | "
f"Confidence: {entity.get('confidence', 0.0):.2f}")
```
=== "Named Entity Recognizer"
Use the NamedEntityRecognizer class for advanced control:
```python
from semantica.semantic_extract import NamedEntityRecognizer
# Initialize with custom configuration
ner = NamedEntityRecognizer(
methods=["spacy", "rule-based"], # Use multiple methods
confidence_threshold=0.7,
merge_overlapping=True
)
text = "Elon Musk announced that Tesla will invest $10B in Texas."
# Extract entities with detailed output
entities = ner.extract_entities(text)
for entity in entities:
print(f"""
Entity: {entity['text']}
Type: {entity['type']}
Start: {entity['start_char']}, End: {entity['end_char']}
Confidence: {entity['confidence']:.2f}
Method: {entity.get('extraction_method', 'N/A')}
""")
```
=== "Custom Entity Types" Define and extract custom entity types for your domain:
```python
from semantica.semantic_extract import (
NamedEntityRecognizer,
CustomEntityDetector
)
# Define custom entity patterns for a medical domain
custom_detector = CustomEntityDetector(
patterns={
"Drug": [
r"\b(aspirin|ibuprofen|acetaminophen)\b",
r"\b\w+(?:mycin|cillin|phen)\b" # Common drug suffixes
],
"Dosage": [
r"\d+\s*(?:mg|ml|g|mcg)\b",
r"\b(?:once|twice|three times)\s+(?:daily|weekly)\b"
],
"Condition": [
r"\b(?:diabetes|hypertension|arthritis)\b",
r"\b\w+itis\b" # Inflammation conditions
]
}
)
ner = NamedEntityRecognizer(
custom_detectors=[custom_detector],
include_standard_types=True # Also extract Person, Org, etc.
)
medical_text = """
Patient prescribed ibuprofen 400mg twice daily for arthritis.
Previous treatment with amoxicillin was discontinued.
"""
entities = ner.extract_entities(medical_text)
# Group by type
from collections import defaultdict
by_type = defaultdict(list)
for e in entities:
by_type[e['type']].append(e['text'])
for entity_type, items in by_type.items():
print(f"{entity_type}: {', '.join(items)}")
# Output:
# Drug: ibuprofen, amoxicillin
# Dosage: 400mg, twice daily
# Condition: arthritis
```
=== "LLM-Enhanced Extraction" Use LLMs for context-aware, high-accuracy extraction:
```python
from semantica.semantic_extract import (
NamedEntityRecognizer,
LLMEnhancer,
create_provider
)
# Create LLM provider (supports OpenAI, Anthropic, Groq, Ollama)
provider = create_provider(
provider_type="openai",
model="gpt-4o",
api_key="your-api-key" # Or use OPENAI_API_KEY env var
)
# Initialize LLM-enhanced NER
enhancer = LLMEnhancer(provider=provider)
ner = NamedEntityRecognizer(
llm_enhancer=enhancer,
use_llm_verification=True # Verify with LLM
)
# Extract with LLM enhancement for ambiguous cases
text = """
Apple reported record revenue. Tim said the new Apple Watch
and the partnership with Goldman Sachs exceeded expectations.
"""
entities = ner.extract_entities(
text,
context="Financial earnings report" # Provide context for better accuracy
)
# LLM helps distinguish:
# - "Apple" (company) vs "apple" (fruit)
# - "Tim" (person - Tim Cook) from context
# - "Goldman Sachs" (organization)
# - "Apple Watch" (product vs organization)
```
=== "Batch Processing" Process multiple documents efficiently:
```python
from semantica.semantic_extract import NamedEntityRecognizer
ner = NamedEntityRecognizer(batch_size=32)
documents = [
"Microsoft acquired Activision for $69 billion.",
"Google announced Gemini AI at their Mountain View headquarters.",
"Amazon's Andy Jassy unveiled new AWS services in Seattle.",
# ... hundreds more documents
]
# Process in batches for efficiency
all_entities = []
for i, doc in enumerate(documents):
entities = ner.extract_entities(doc)
all_entities.extend([
{**e, "doc_id": i} for e in entities
])
if (i + 1) % 100 == 0:
print(f"Processed {i + 1}/{len(documents)} documents")
from collections import Counter
entity_types = Counter(e['type'] for e in all_entities)
print(f"Entity Distribution:")
for etype, count in entity_types.most_common():
print(f" {etype}: {count}")
```
Related Modules:
semantic_extractModule - Entity and relationship extractionontologyModule - Custom entity type definitions
3. Relationship Extraction
!!! abstract "Definition" Relationship Extraction is the process of identifying and extracting semantic relationships between entities in text. It connects entities to form meaningful knowledge structures.
Relationship Types:
=== "Semantic Relationships" Relationships that define meaning and connection between entities.
- `works_for` - Employment relationships
- `located_in` - Geographic relationships
- `founded_by` - Creation relationships
- `owns` - Ownership relationships
- `part_of` - Hierarchical relationships
```python
# Example: "Tim Cook works for Apple Inc."
# Extracted: (Tim Cook) --[works_for]--> (Apple Inc.)
```
=== "Temporal Relationships" Relationships defined by time and sequence.
- `happened_before` - Temporal precedence
- `happened_after` - Temporal succession
- `during` - Temporal containment
- `overlaps_with` - Temporal overlap
```python
# Example: "WWDC 2023 happened before WWDC 2024"
# Extracted: (WWDC 2023) --[happened_before]--> (WWDC 2024)
```
=== "Causal Relationships" Cause and effect relationships.
- `causes` - Direct causation
- `results_in` - Outcome relationships
- `prevents` - Prevention relationships
- `influences` - Indirect influence
```python
# Example: "High inflation causes economic instability"
# Extracted: (High inflation) --[causes]--> (Economic instability)
```
Visual Example:
graph LR
A[Apple Inc.] -->|founded_by| B[Steve Jobs]
A -->|located_in| C[Cupertino]
A -->|has_ceo| D[Tim Cook]
C -->|in_state| E[California]
B -->|co-founded| F[Apple Inc.]
style A fill:#e3f2fd
style B fill:#fff3e0
style C fill:#f3e5f5
style D fill:#fff3e0
style E fill:#f3e5f5
style F fill:#e3f2fd
Practical Examples:
=== "Basic Relation Extraction" Extract relationships between entities:
```python
from semantica.semantic_extract import RelationExtractor, NamedEntityRecognizer
text = """
Tim Cook became CEO of Apple Inc. in 2011, succeeding Steve Jobs.
Apple is headquartered in Cupertino, California. The company
acquired Beats Electronics in 2014 for $3 billion.
"""
# First extract entities
ner = NamedEntityRecognizer()
entities = ner.extract_entities(text)
# Then extract relationships
rel_extractor = RelationExtractor()
relations = rel_extractor.extract_relations(text, entities=entities)
print("Extracted Relationships:")
for rel in relations:
print(f" ({rel['source']}) --[{rel['type']}]--> ({rel['target']})")
print(f" Confidence: {rel.get('confidence', 0.0):.2f}")
```
=== "RelationExtractor Class"
Use the RelationExtractor for advanced control:
```python
from semantica.semantic_extract import (
RelationExtractor,
NamedEntityRecognizer
)
# First extract entities
ner = NamedEntityRecognizer()
text = """
Dr. Sarah Chen published her research on quantum computing at MIT.
Her work was funded by DARPA and received the ACM Award in 2023.
"""
entities = ner.extract_entities(text)
# Then extract relationships
rel_extractor = RelationExtractor(
relation_types=["works_at", "funded_by", "received", "published"],
bidirectional=False,
confidence_threshold=0.6
)
relations = rel_extractor.extract_relations(text, entities=entities)
print("Knowledge Graph Edges:")
for rel in relations:
arrow = "<->" if rel.get('bidirectional') else "->"
print(f" {rel['source_text']} {arrow} {rel['target_text']}")
print(f" Relation: {rel['type']}")
```
=== "Triple Extraction (RDF)" Extract subject-predicate-object triples for RDF/semantic web:
```python
from semantica.semantic_extract import (
TripleExtractor,
RDFSerializer,
TripleValidator
)
text = """
Albert Einstein was born in Ulm, Germany in 1879. He developed
the theory of relativity and won the Nobel Prize in Physics in 1921.
Einstein worked at Princeton University until his death in 1955.
"""
# Extract RDF-style triples
extractor = TripleExtractor(
include_temporal=True, # Include time information
include_provenance=True # Track source sentences
)
triples = extractor.extract_triples(text)
print("Extracted Triples (Subject-Predicate-Object):")
for triple in triples:
print(f" Subject: {triple['subject']}")
print(f" Predicate: {triple['predicate']}")
print(f" Object: {triple['object']}")
if triple.get('temporal'):
print(f" When: {triple['temporal']}")
print()
validator = TripleValidator()
validation = validator.validate(triples)
print(f"Valid triples: {validation['valid_count']}/{len(triples)}")
serializer = RDFSerializer(format="turtle")
turtle_output = serializer.serialize(
triples,
base_uri="https://example.org/knowledge/"
)
print("Turtle Output:")
print(turtle_output)
```
=== "Event Detection" Extract events with temporal information:
```python
from semantica.semantic_extract import (
EventDetector,
TemporalEventProcessor
)
news_text = """
On March 15, 2024, SpaceX successfully launched Starship from
Boca Chica, Texas. The rocket reached orbit before splashing down
in the Indian Ocean. CEO Elon Musk announced plans for a Mars
mission by 2026.
"""
# Detect events
detector = EventDetector(
event_types=["launch", "announcement", "achievement"],
extract_participants=True,
extract_location=True,
extract_time=True
)
events = detector.detect_events(news_text)
print("Detected Events:")
for event in events:
print(f" Event: {event['description']}")
print(f" Type: {event['type']}")
print(f" When: {event.get('datetime', 'Unknown')}")
print(f" Where: {event.get('location', 'Unknown')}")
print(f" Who: {', '.join(event.get('participants', []))}")
print()
# Process temporal relationships between events
temporal = TemporalEventProcessor()
timeline = temporal.build_timeline(events)
print("Event Timeline:")
for i, evt in enumerate(timeline, 1):
print(f" {i}. {evt['datetime']}: {evt['description']}")
```
=== "Coreference Resolution" Resolve pronouns and entity references:
```python
from semantica.semantic_extract import CoreferenceResolver
text = """
Apple Inc. announced their new iPhone. The company said it would
be available in September. Tim Cook presented the device at their
headquarters. He emphasized its improved camera capabilities.
"""
resolver = CoreferenceResolver()
result = resolver.resolve(text)
print("Coreference Chains:")
for chain in result['chains']:
print(f" Entity: {chain['canonical']}")
print(f" Mentions: {', '.join(chain['mentions'])}")
print()
resolved_text = resolver.get_resolved_text(text)
print("Resolved Text:")
print(resolved_text)
```
Related Modules:
semantic_extractModule - Relationship extractionkgModule - Building graphs from relationships
4. Embeddings
!!! abstract "Definition" Embeddings are dense vector representations of text, images, or other data that capture semantic meaning in a continuous vector space. They enable machines to understand similarity and meaning.
!!! note "The Bridge Between Language and Understanding" Embeddings are the bridge between human language and machine understanding. They convert text into numerical vectors that preserve semantic relationships.
How Embeddings Work:
Embeddings convert text into numerical vectors that capture semantic meaning. Similar texts have similar vectors, enabling semantic search and similarity calculations.
Example:
Text: "machine learning"
Embedding: [0.123, -0.456, 0.789, ..., 0.234] # (vector of 1536 dimensions)
# Similar texts will have similar vectors
"artificial intelligence" → [0.145, -0.432, 0.801, ..., 0.221] # Close in vector space
"cooking recipes" → [-0.234, 0.567, -0.123, ..., -0.456] # Far in vector space
Embedding Providers:
| Provider | Model | Dimensions | Speed | Cost | Best For |
|---|---|---|---|---|---|
| OpenAI | text-embedding-3-large | 3072 | Fast | Paid | Production, high accuracy |
| OpenAI | text-embedding-3-small | 1536 | Fast | Paid | Balanced performance |
| Cohere | embed-english-v3.0 | 1024 | Fast | Paid | Multilingual support |
| HuggingFace | sentence-transformers | 384-768 | Medium | Free | Development, open source |
| Local | Various | Variable | Slow | Free | Privacy, offline use |
Practical Examples:
=== "Quick Start" Generate embeddings in one call:
```python
from semantica.embeddings import EmbeddingGenerator, embed_text
# Using EmbeddingGenerator class
texts = [
"Machine learning is transforming industries",
"Artificial intelligence powers modern applications",
"Deep learning enables image recognition",
"Cooking recipes are delicious" # Different topic
]
generator = EmbeddingGenerator()
# Generate embeddings for multiple texts
embeddings = []
for text in texts:
emb = generator.generate_embeddings(text, data_type="text")
embeddings.append(emb)
print(f"Generated {len(embeddings)} embeddings")
print(f"Dimensions: {embeddings[0].shape}")
# Or use the convenience function for single text
embedding = embed_text("Hello, world!", method="sentence_transformers")
print(f"Single embedding shape: {embedding.shape}")
```
=== "Similarity Search" Calculate semantic similarity between texts:
```python
from semantica.embeddings import (
EmbeddingGenerator,
calculate_similarity
)
import numpy as np
generator = EmbeddingGenerator()
# Generate embeddings
query = "How does photosynthesis work?"
documents = [
"Plants convert sunlight into energy through photosynthesis",
"The process of photosynthesis occurs in chloroplasts",
"Dogs are popular household pets",
"Solar panels convert sunlight to electricity",
"Chlorophyll absorbs light for photosynthesis"
]
query_emb = generator.generate_embeddings(query, data_type="text")
doc_embs = [generator.generate_embeddings(d, data_type="text") for d in documents]
print("Semantic Similarity Search:")
print(f"Query: \"{query}\"")
similarities = []
for i, doc_emb in enumerate(doc_embs):
sim = calculate_similarity(query_emb, doc_emb, method="cosine")
similarities.append((sim, documents[i]))
for sim, doc in sorted(similarities, reverse=True):
relevance = "HIGH" if sim > 0.6 else "MED" if sim > 0.3 else "LOW"
print(f" [{relevance}] {sim:.3f}: {doc[:50]}...")
```
=== "Multiple Embedding Types" Work with text, image, and audio embeddings:
```python
from semantica.embeddings import (
TextEmbedder,
ImageEmbedder,
AudioEmbedder,
MultimodalEmbedder
)
text_embedder = TextEmbedder(model="all-MiniLM-L6-v2")
text_emb = text_embedder.embed("A beautiful sunset over the ocean")
print(f"Text embedding: {text_emb.shape}")
image_embedder = ImageEmbedder(model="clip")
image_emb = image_embedder.embed("sunset_photo.jpg")
print(f"Image embedding: {image_emb.shape}")
audio_embedder = AudioEmbedder()
audio_emb = audio_embedder.embed("ocean_waves.mp3")
print(f"Audio embedding: {audio_emb.shape}")
multimodal = MultimodalEmbedder()
combined = multimodal.embed(
text="sunset over ocean",
image="sunset_photo.jpg",
strategy="concatenate"
)
print(f"Combined embedding: {combined.shape}")
from semantica.embeddings import calculate_similarity
text_query = text_embedder.embed("beach at sunset")
images = ["beach.jpg", "mountain.jpg", "city.jpg"]
for img in images:
img_emb = image_embedder.embed(img)
sim = calculate_similarity(text_query, img_emb, method="cosine")
print(f" {img}: {sim:.3f}")
```
=== "Embedding Optimization" Optimize embeddings for storage and performance:
```python
from semantica.embeddings import EmbeddingOptimizer
import numpy as np
optimizer = EmbeddingOptimizer()
# Generate some embeddings
embeddings = np.random.randn(1000, 768).astype(np.float32)
print(f"Original size: {embeddings.nbytes / 1024:.1f} KB")
# Dimension reduction with PCA
reduced = optimizer.reduce_dimensions(
embeddings,
method="pca",
target_dims=256
)
print(f"After PCA (256d): {reduced.nbytes / 1024:.1f} KB")
# Quantization for storage efficiency
quantized = optimizer.quantize(
embeddings,
bits=8 # 8-bit quantization
)
print(f"After 8-bit quantization: {quantized.nbytes / 1024:.1f} KB")
# Normalize for cosine similarity
normalized = optimizer.normalize(embeddings, method="l2")
print(f"Normalized: unit vectors ready for dot product")
```
=== "Pooling Strategies" Aggregate embeddings using different strategies:
```python
from semantica.embeddings import (
MeanPooling,
MaxPooling,
AttentionPooling,
HierarchicalPooling
)
import numpy as np
# Simulate token-level embeddings (e.g., from a transformer)
# Shape: (sequence_length, embedding_dim)
token_embeddings = np.random.randn(128, 768)
# Mean pooling - average all tokens
mean_pool = MeanPooling()
mean_result = mean_pool.pool(token_embeddings)
print(f"Mean pooling: {mean_result.shape}") # (768,)
# Max pooling - take max across tokens
max_pool = MaxPooling()
max_result = max_pool.pool(token_embeddings)
print(f"Max pooling: {max_result.shape}") # (768,)
# Attention-based pooling - learned weights
attn_pool = AttentionPooling(hidden_dim=768)
attn_result = attn_pool.pool(token_embeddings)
print(f"Attention pooling: {attn_result.shape}") # (768,)
# Hierarchical pooling for long documents
hier_pool = HierarchicalPooling(chunk_size=32)
document_embeddings = np.random.randn(10, 128, 768) # 10 paragraphs
hier_result = hier_pool.pool(document_embeddings)
print(f"Hierarchical pooling: {hier_result.shape}") # (768,)
```
=== "Context Management" Handle long texts with context windows:
```python
from semantica.embeddings import ContextManager, EmbeddingGenerator
# Long document that exceeds model context
long_document = "..." * 10000 # Very long text
# Manage context windows
context_manager = ContextManager(
max_window_size=512, # Tokens per window
overlap=50, # Overlap between windows
preserve_sentences=True
)
windows = context_manager.split(long_document)
print(f"Split into {len(windows)} windows")
# Generate embeddings for each window
generator = EmbeddingGenerator()
window_embeddings = []
for i, window in enumerate(windows):
emb = generator.generate_embeddings(window.text, data_type="text")
window_embeddings.append({
'embedding': emb,
'start_char': window.start_char,
'end_char': window.end_char
})
# Merge windows for document-level embedding
from semantica.embeddings import pool_embeddings
import numpy as np
all_embs = np.array([w['embedding'] for w in window_embeddings])
doc_embedding = pool_embeddings(all_embs, method="mean")
print(f"Document embedding shape: {doc_embedding.shape}")
```
Related Modules:
embeddingsModule - Embedding generationvector_storeModule - Vector storage and search
5. Temporal Graphs
!!! abstract "Definition" Temporal Graphs are knowledge graphs that track changes over time, allowing queries about the state of the graph at specific time points. They enable time-aware reasoning and analysis.
Key Features:
- Time-stamped Entities: Entities have creation and modification timestamps
- Time-stamped Relationships: Relationships have validity periods
- Historical Queries: Query the graph state at any point in time
- Change Tracking: Track how entities and relationships evolve
Visual Timeline:
timeline
title Temporal Graph Evolution
2020 : Entity A created
: Relationship A->B established
2021 : Entity B updated
: Relationship B->C created
2022 : Entity A deleted
: New Relationship D->C
2023 : Entity C properties updated
: Relationship A->B expired
Practical Examples:
=== "Building Temporal Graphs" Create a knowledge graph with time-aware edges:
```python
from semantica.kg import GraphBuilder
from datetime import datetime, timedelta
# Build a temporal knowledge graph
builder = GraphBuilder(
enable_temporal=True,
temporal_granularity="day", # day, hour, minute, second
track_history=True,
version_snapshots=True
)
# Add time-stamped data
sources = [{
"entities": [
{
"id": "e1",
"text": "Satya Nadella",
"type": "Person",
"valid_from": "2014-02-04" # Became CEO
},
{
"id": "e2",
"text": "Microsoft",
"type": "Organization"
},
{
"id": "e3",
"text": "Steve Ballmer",
"type": "Person",
"valid_from": "2000-01-13",
"valid_until": "2014-02-04" # Was CEO until
}
],
"relationships": [
{
"source": "e1",
"target": "e2",
"type": "CEO_OF",
"valid_from": "2014-02-04",
"valid_until": None # Still active
},
{
"source": "e3",
"target": "e2",
"type": "CEO_OF",
"valid_from": "2000-01-13",
"valid_until": "2014-02-04"
}
]
}]
kg = builder.build(sources)
print(f"Built temporal graph with {kg['metadata']['num_entities']} entities")
```
=== "Temporal Queries" Query the graph at specific points in time:
```python
from semantica.kg import TemporalGraphQuery
from datetime import datetime
# Initialize temporal query engine
temporal_query = TemporalGraphQuery(kg)
# Query 1: Who was CEO in 2010?
state_2010 = temporal_query.query_at_time(
datetime(2010, 6, 1),
query_type="relationships",
relationship_type="CEO_OF"
)
print(f"CEO in 2010: {state_2010['results']}")
state_2020 = temporal_query.query_at_time(
datetime(2020, 6, 1),
query_type="relationships",
relationship_type="CEO_OF"
)
print(f"CEO in 2020: {state_2020['results']}")
ceo_history = temporal_query.query_time_range(
start_time=datetime(2000, 1, 1),
end_time=datetime(2024, 1, 1),
relationship_type="CEO_OF"
)
print("CEO History:")
for rel in ceo_history['results']:
print(f" {rel['source']} -> {rel['valid_from']} to {rel.get('valid_until', 'present')}")
```
=== "Pattern Detection" Detect temporal patterns in your graph:
```python
from semantica.kg import TemporalPatternDetector
# Initialize pattern detector
detector = TemporalPatternDetector(kg)
# Detect sequences: A happened, then B, then C
sequences = detector.detect_sequences(
event_types=["HIRED", "PROMOTED", "RESIGNED"],
max_gap_days=365 # Events must be within 1 year
)
print("Detected Sequences:")
for seq in sequences:
print(f" {' -> '.join(seq['events'])}")
print(f" Entity: {seq['entity']}")
print(f" Duration: {seq['duration_days']} days")
cycles = detector.detect_cycles(
min_occurrences=2,
relationship_types=["QUARTERLY_REPORT"]
)
print("Detected Cycles:")
for cycle in cycles:
print(f" Pattern: {cycle['pattern']}")
print(f" Period: {cycle['period_days']} days")
print(f" Occurrences: {cycle['count']}")
trends = detector.detect_trends(
metric="employee_count",
entity_type="Organization",
window_days=90
)
for trend in trends:
direction = "UP" if trend['direction'] == 'increasing' else "DOWN"
print(f"[{direction}] {trend['entity']}: {trend['change_percent']:.1f}% over {trend['period']}")
```
=== "Version Management" Track and manage graph versions over time:
```python
from semantica.kg import TemporalVersionManager
from datetime import datetime
# Initialize version manager
version_mgr = TemporalVersionManager(kg)
# Create a snapshot of current state
snapshot_id = version_mgr.create_snapshot(
description="Q4 2024 knowledge graph",
timestamp=datetime.now()
)
print(f"Created snapshot: {snapshot_id}")
diff = version_mgr.compare_versions(
version_a=snapshot_id,
version_b="current"
)
print("Changes since snapshot:")
print(f" Added entities: {len(diff['added_entities'])}")
print(f" Removed entities: {len(diff['removed_entities'])}")
print(f" Modified entities: {len(diff['modified_entities'])}")
print(f" Added relationships: {len(diff['added_relationships'])}")
print(f" Removed relationships: {len(diff['removed_relationships'])}")
evolution = version_mgr.analyze_evolution(
start_date=datetime(2024, 1, 1),
end_date=datetime(2024, 12, 31)
)
print("Graph Evolution (2024):")
print(f" Total snapshots: {evolution['snapshot_count']}")
print(f" Net entity growth: {evolution['entity_growth']}")
print(f" Net relationship growth: {evolution['relationship_growth']}")
```
=== "Graph Evolution Analysis" Analyze how your knowledge graph changes over time:
```python
from semantica.kg import GraphAnalyzer
analyzer = GraphAnalyzer()
# Analyze temporal evolution
evolution = analyzer.analyze_temporal_evolution(
kg,
time_periods=[
("2022-01-01", "2022-06-30"),
("2022-07-01", "2022-12-31"),
("2023-01-01", "2023-06-30"),
("2023-07-01", "2023-12-31")
]
)
print("Graph Evolution Analysis:")
for period in evolution['periods']:
print(f"Period: {period['start']} to {period['end']}")
print(f" Entities: {period['entity_count']} (+{period['entity_growth']})")
print(f" Relationships: {period['relationship_count']} (+{period['rel_growth']})")
print(f" Density: {period['density']:.4f}")
print(f" New entity types: {period['new_types']}")
print()
stability = analyzer.calculate_stability(kg, window_days=30)
print(f"Graph Stability Score: {stability['score']:.2f}")
print(f" Most stable entities: {stability['most_stable'][:3]}")
print(f" Most volatile entities: {stability['most_volatile'][:3]}")
```
Related Modules:
kgModule - Temporal graph supportvisualizationModule - Temporal visualization
6. GraphRAG
!!! abstract "Definition" GraphRAG (Graph-Augmented Retrieval Augmented Generation) is an advanced RAG approach that combines vector search with knowledge graph traversal to provide more accurate and contextually relevant information to LLMs.
How GraphRAG Works:
flowchart TD
subgraph Query["Query Processing"]
Q[User Query] --> VS[Vector Search]
Q --> KE[Keyword Extraction]
end
subgraph Retrieval["Hybrid Retrieval"]
VS --> Docs[Relevant Documents]
KE --> Nodes[Start Nodes]
Nodes --> Trav[Graph Traversal]
Trav --> Context[Graph Context]
end
subgraph Synthesis["Answer Generation"]
Docs --> Prompt[Enhanced Prompt]
Context --> Prompt
Prompt --> LLM[LLM Generation]
LLM --> A[Accurate Answer]
end
style Q fill:#e1f5fe
style LLM fill:#e8f5e9
style A fill:#fff9c4
Advantages over Traditional RAG:
| Feature | Traditional RAG | GraphRAG |
|---|---|---|
| Query Understanding | Keyword matching | Semantic + structural |
| Context Retrieval | Document chunks | Documents + relationships |
| Answer Accuracy | Good | Better (grounded in graph) |
| Hallucination Risk | Medium | Lower |
| Complex Queries | Limited | Excellent |
| Relationship Awareness | No | Yes |
Practical Examples:
=== "Basic GraphRAG" Build a knowledge base and query with GraphRAG:
```python
from semantica import Semantica
from semantica.vector_store import VectorStore, store_vectors, search_vectors
from semantica.semantic_extract import NamedEntityRecognizer
from semantica.embeddings import embed_text
# Initialize Semantica
semantica = Semantica()
# Build knowledge base from documents
result = semantica.build_knowledge_base(
sources=["company_docs/", "research_papers/"],
embeddings=True,
graph=True
)
kg = result["knowledge_graph"]
embeddings_data = result["embeddings"]
# Initialize vector store
vector_store = VectorStore(backend="faiss", dimension=768)
# Store embeddings
vector_ids = vector_store.store_vectors(
vectors=embeddings_data["vectors"],
metadata=embeddings_data["metadata"]
)
# Initialize NER for query processing
ner = NamedEntityRecognizer()
# Process a query
query = "Who is the CEO of Apple?"
# Get query embedding and search
query_embedding = embed_text(query, method="sentence_transformers")
vector_results = vector_store.search(query_embedding, k=5)
# Extract entities from query
query_entities = ner.extract_entities(query)
# Get graph context for query entities
graph_context = []
for entity in query_entities:
for rel in kg["relationships"]:
if entity["text"].lower() in rel.get("source_text", "").lower():
graph_context.append(rel)
print(f"Vector results: {len(vector_results)}")
print(f"Graph context: {len(graph_context)} relevant relationships")
```
=== "Context Retrieval" Use the context module for agent memory and retrieval:
```python
from semantica.context import (
ContextRetriever,
AgentMemory,
ContextGraphBuilder,
build_context
)
from semantica.vector_store import VectorStore
# Initialize vector store
vector_store = VectorStore(backend="faiss", dimension=768)
# Build context graph
context_result = build_context(
entities=kg["entities"],
relationships=kg["relationships"],
vector_store=vector_store,
knowledge_graph=kg,
store_initial_memories=True
)
print(f"Built context with {context_result['statistics']['graph'].get('node_count', 0)} nodes")
# Initialize agent memory
memory = AgentMemory(
vector_store=vector_store,
knowledge_graph=kg
)
# Store facts in memory
memory_id = memory.store(
"Tim Cook is the CEO of Apple Inc.",
metadata={"type": "fact", "source": "document"}
)
print(f"Stored memory: {memory_id}")
# Initialize context retriever
retriever = ContextRetriever(
vector_store=vector_store,
knowledge_graph=kg
)
# Retrieve relevant context
results = retriever.retrieve(
query="Who leads Apple?",
max_results=10
)
print("Retrieved context:")
for r in results:
print(f" {r.content} (score: {r.score:.2f})")
```
=== "Hybrid Search" Combine vector and metadata search:
```python
from semantica.vector_store import (
VectorStore,
HybridSearch,
MetadataFilter,
MetadataStore
)
from semantica.embeddings import embed_text
# Initialize stores
vector_store = VectorStore(backend="faiss", dimension=768)
metadata_store = MetadataStore()
hybrid_search = HybridSearch()
# Generate query embedding
query_embedding = embed_text("Apple CEO responsibilities", method="sentence_transformers")
# Create metadata filter
metadata_filter = MetadataFilter()
metadata_filter.eq("entity_type", "Person")
metadata_filter.eq("relationship", "CEO_OF")
# Get stored data
all_vectors = vector_store.get_all_vectors()
all_metadata = metadata_store.get_all()
all_ids = vector_store.get_all_ids()
# Perform hybrid search
results = hybrid_search.search(
query_vector=query_embedding,
vectors=all_vectors,
metadata=all_metadata,
vector_ids=all_ids,
filter=metadata_filter,
top_k=10
)
print("Hybrid search results:")
for result in results:
print(f" ID: {result['id']}, Score: {result['score']:.3f}")
```
=== "Reasoning with Inference" Use the reasoning module for graph inference:
```python
from semantica.reasoning import (
InferenceEngine,
RuleManager,
Rule,
ExplanationGenerator
)
# Initialize engine and rule manager
engine = InferenceEngine()
rule_manager = RuleManager()
# Add inference rules
rule_manager.add_rule(Rule(
name="ceo_works_for",
conditions=["?person CEO_OF ?company"],
conclusions=["?person WORKS_FOR ?company"],
priority=1
))
rule_manager.add_rule(Rule(
name="transitivity",
conditions=["?a PART_OF ?b", "?b PART_OF ?c"],
conclusions=["?a PART_OF ?c"],
priority=2
))
# Define facts
facts = [
{"subject": "Tim Cook", "predicate": "CEO_OF", "object": "Apple"},
{"subject": "iPhone", "predicate": "PART_OF", "object": "Apple"},
{"subject": "A15 Chip", "predicate": "PART_OF", "object": "iPhone"}
]
# Run inference
inferred = engine.infer(
facts=facts,
rules=rule_manager.get_rules(),
strategy="forward"
)
print(f"Original facts: {len(facts)}")
print(f"After inference: {len(inferred.facts)}")
# Generate explanations
explainer = ExplanationGenerator()
for new_fact in inferred.new_facts:
explanation = explainer.generate_explanation(
conclusion=new_fact,
reasoning_path=inferred.reasoning_path
)
print(f" {new_fact}: {explanation.summary}")
```
=== "LLM-Enhanced Retrieval" Integrate with LLM providers for answer generation:
```python
from semantica import Semantica
from semantica.semantic_extract import create_provider, OpenAIProvider
from semantica.context import ContextRetriever
from semantica.vector_store import VectorStore
# Initialize components
vector_store = VectorStore(backend="faiss", dimension=768)
# Create LLM provider
provider = create_provider(
provider_type="openai",
model="gpt-4o"
)
# Initialize context retriever
retriever = ContextRetriever(
vector_store=vector_store,
knowledge_graph=kg
)
# Define question
question = "What products does Apple manufacture?"
# Retrieve relevant context
context = retriever.retrieve(query=question, max_results=5)
# Format context for prompt
context_text = "\n".join([
f"- {c.content}" for c in context
])
# Build prompt
prompt = f"""Based on this context, answer the question.
Context: {context_text}
Question: {question}
Answer:"""
# Generate response
response = provider.generate(prompt)
print(f"Answer: {response}")
```
Related Modules:
kgModule - Knowledge graph constructionvector_storeModule - Vector searchreasoningModule - Graph reasoning
7. Ontology
!!! abstract "Definition" An Ontology is a formal specification of concepts, relationships, and constraints in a domain, typically expressed in OWL (Web Ontology Language). It defines the schema and structure of your knowledge domain.
Key Components:
- Classes: Categories of entities (e.g.,
Person,Company,Location) - Properties: Relationships and attributes (e.g.,
worksFor,hasName) - Individuals: Specific instances (e.g.,
John Doe,Apple Inc.) - Axioms: Rules and constraints (e.g., "A Person can only workFor one Company")
Ontology Structure:
classDiagram
class Person {
+String name
+Date birthDate
+worksFor Company
}
class Company {
+String name
+Date founded
+locatedIn Location
}
class Location {
+String city
+String country
}
Person --> Company : worksFor
Company --> Location : locatedIn
Practical Examples:
=== "Automatic Ontology Generation" Generate an ontology from your knowledge graph:
```python
from semantica.ontology import OntologyGenerator, generate_ontology
# Your knowledge graph data
kg_data = {
"entities": [
{"id": "e1", "text": "Apple Inc.", "type": "Company"},
{"id": "e2", "text": "Tim Cook", "type": "Person"},
{"id": "e3", "text": "iPhone", "type": "Product"},
{"id": "e4", "text": "Cupertino", "type": "Location"}
],
"relationships": [
{"source": "e2", "target": "e1", "type": "CEO_OF"},
{"source": "e1", "target": "e3", "type": "MANUFACTURES"},
{"source": "e1", "target": "e4", "type": "HEADQUARTERED_IN"}
]
}
# Method 1: Using convenience function
ontology = generate_ontology(kg_data, method="default")
print(f"Quick generation: {len(ontology.get('classes', []))} classes")
# Method 2: Using OntologyGenerator class for more control
generator = OntologyGenerator(
base_uri="https://example.org/ontology/",
version="1.0.0"
)
ontology = generator.generate_ontology(kg_data)
print(f"Generated Ontology:")
print(f" Classes: {len(ontology['classes'])}")
print(f" Properties: {len(ontology['properties'])}")
print(f" Axioms: {len(ontology.get('axioms', []))}")
# Display class details
print("Classes:")
for cls in ontology['classes']:
print(f" {cls['name']}")
print(f" URI: {cls['uri']}")
if cls.get('parent'):
print(f" Parent: {cls['parent']}")
```
=== "Class and Property Inference" Automatically infer classes and properties from data:
```python
from semantica.ontology import (
ClassInferrer,
PropertyGenerator,
infer_classes,
infer_properties
)
# Sample entities
entities = [
{"text": "Apple", "type": "Company"},
{"text": "Microsoft", "type": "Company"},
{"text": "Google", "type": "Company"},
{"text": "Tim Cook", "type": "Person"},
{"text": "Satya Nadella", "type": "Person"},
{"text": "iPhone", "type": "Product"},
{"text": "Windows", "type": "Product"},
]
# Method 1: Using convenience function
classes = infer_classes(entities, method="default")
# Method 2: Using ClassInferrer for more control
class_inferrer = ClassInferrer(
min_occurrence=2, # Minimum entities to create a class
merge_similar=True,
similarity_threshold=0.8
)
classes = class_inferrer.infer_classes(entities)
print("Inferred Classes:")
for cls in classes:
print(f" {cls['name']}: {cls['instance_count']} instances")
# Infer properties from relationships
relationships = [
{"source_type": "Person", "target_type": "Company", "type": "CEO_OF"},
{"source_type": "Company", "target_type": "Product", "type": "MANUFACTURES"},
{"source_type": "Person", "target_type": "Company", "type": "WORKS_FOR"},
]
# Using PropertyGenerator
prop_generator = PropertyGenerator()
properties = prop_generator.infer_properties(entities, relationships, classes)
print("Inferred Properties:")
for prop in properties:
print(f" {prop['name']}")
print(f" Domain: {prop['domain']} -> Range: {prop['range']}")
print(f" Type: {prop['property_type']}")
```
=== "OWL/RDF Generation" Generate OWL ontologies in various formats:
```python
from semantica.ontology import OWLGenerator, generate_owl
# Initialize OWL generator
owl_gen = OWLGenerator(
base_uri="https://example.org/onto/",
output_format="turtle" # turtle, rdf-xml, json-ld, n3
)
# Define ontology classes
owl_gen.add_class("Person", parent="Thing")
owl_gen.add_class("Company", parent="Thing")
owl_gen.add_class("CEO", parent="Person")
# Add object properties
owl_gen.add_object_property(
name="worksFor",
domain="Person",
range="Company"
)
owl_gen.add_object_property(
name="isCEOOf",
domain="CEO",
range="Company",
functional=True # A CEO can only be CEO of one company
)
# Add data properties
owl_gen.add_data_property(
name="hasName",
domain="Person",
range="xsd:string"
)
owl_gen.add_data_property(
name="foundedYear",
domain="Company",
range="xsd:integer"
)
# Add axioms/constraints
owl_gen.add_axiom(
class_name="CEO",
axiom="SubClassOf: worksFor exactly 1 Company"
)
# Generate Turtle output
turtle_output = owl_gen.serialize()
print(turtle_output)
# Example output:
# @prefix : <https://example.org/onto/> .
# @prefix owl: <http://www.w3.org/2002/07/owl#> .
# :Person a owl:Class .
# :Company a owl:Class .
# :CEO a owl:Class ; rdfs:subClassOf :Person .
# Save to file
owl_gen.save("company_ontology.ttl")
print("Ontology saved to company_ontology.ttl")
```
=== "Ontology Validation" Validate ontologies with symbolic reasoners:
```python
from semantica.ontology import OntologyValidator, validate_ontology
# Load or create ontology
ontology_file = "company_ontology.ttl"
# Method 1: Using the convenience function
result = validate_ontology(ontology_file, method="default")
print(f"Quick validation: {'Valid' if result['valid'] else 'Invalid'}")
# Method 2: Using OntologyValidator for more control
validator = OntologyValidator(
reasoner="hermit", # hermit, pellet, or auto
check_consistency=True,
check_satisfiability=True
)
# Validate the ontology
validation_result = validator.validate(ontology_file)
print(f"Validation Results:")
print(f" Consistent: {'Yes' if validation_result.consistent else 'No'}")
print(f" Satisfiable: {'Yes' if validation_result.satisfiable else 'No'}")
if validation_result.errors:
print(f"Errors ({len(validation_result.errors)}):")
for error in validation_result.errors:
print(f" - {error}")
if validation_result.warnings:
print(f"Warnings ({len(validation_result.warnings)}):")
for warning in validation_result.warnings:
print(f" - {warning}")
# Check specific constraints
constraints = [
"Every CEO must work for exactly one Company",
"A Person cannot be their own manager"
]
for constraint in constraints:
is_valid = validator.check_constraint(constraint)
status = "PASS" if is_valid else "FAIL"
print(f" [{status}] {constraint}")
```
=== "Competency Questions" Define and evaluate competency questions:
```python
from semantica.ontology import (
CompetencyQuestionsManager,
CompetencyQuestion,
OntologyEvaluator
)
# Initialize competency questions manager
cq_manager = CompetencyQuestionsManager()
# Add questions your ontology should be able to answer
cq_manager.add_question(CompetencyQuestion(
question="Who is the CEO of a given company?",
category="organizational",
priority=1, # 1=high, 2=medium, 3=low
expected_elements=["Person", "Company", "isCEOOf"]
))
cq_manager.add_question(CompetencyQuestion(
question="What products does a company manufacture?",
category="products",
priority=1,
expected_elements=["Company", "Product", "manufactures"]
))
cq_manager.add_question(CompetencyQuestion(
question="Where is a company headquartered?",
category="location",
priority=2,
expected_elements=["Company", "Location", "headquarteredIn"]
))
# Get all questions
questions = cq_manager.get_questions()
print(f"Defined {len(questions)} competency questions")
# Evaluate ontology against competency questions
evaluator = OntologyEvaluator()
evaluation = evaluator.evaluate(
ontology="company_ontology.ttl",
competency_questions=questions
)
print(f"Ontology Evaluation:")
print(f" Coverage Score: {evaluation.coverage_score:.1%}")
print(f" Completeness Score: {evaluation.completeness_score:.1%}")
print("Question Coverage:")
for result in evaluation.question_results:
status = "YES" if result['answerable'] else "NO"
print(f" [{status}] {result['question']}")
if not result['answerable'] and result.get('missing_elements'):
print(f" Missing: {', '.join(result['missing_elements'])}")
```
=== "Domain Ontologies" Use pre-built domain ontologies:
```python
from semantica.ontology import DomainOntologies, OWLGenerator
# Access pre-built domain ontologies
domains = DomainOntologies()
# List available domains
print("Available Domain Ontologies:")
for domain in domains.list_domains():
print(f" - {domain['name']}: {domain['description']}")
# Load healthcare ontology
healthcare_onto = domains.load("healthcare")
print(f"Healthcare Ontology:")
print(f" Classes: {len(healthcare_onto.get('classes', []))}")
print(f" Properties: {len(healthcare_onto.get('properties', []))}")
# Extend the ontology
owl_gen = OWLGenerator(base_uri="https://example.org/healthcare/")
owl_gen.add_class("MedicalDevice", parent="Equipment")
# Load and merge multiple ontologies
finance_onto = domains.load("finance")
merged = domains.merge([healthcare_onto, finance_onto])
print(f"Merged ontology: {len(merged.get('classes', []))} classes")
# Find alignments between ontologies
alignments = domains.find_alignments(
healthcare_onto,
finance_onto,
similarity_threshold=0.7
)
print(f"Found {len(alignments)} concept alignments:")
for align in alignments[:5]:
print(f" {align['source']} ~ {align['target']} ({align['similarity']:.2f})")
```
Related Modules:
ontologyModule - Ontology generation and managementkgModule - Knowledge graph construction
8. Quality Assurance
!!! abstract "Definition" Quality Assurance encompasses processes and metrics to ensure knowledge graph quality, including completeness, consistency, accuracy, and coverage validation.
Quality Dimensions:
| Dimension | Description | Metrics |
|---|---|---|
| Completeness | Percentage of entities with required properties | Property coverage, missing fields |
| Consistency | Absence of contradictions | Conflict count, validation errors |
| Accuracy | Correctness of extracted information | Precision, recall, F1-score |
| Coverage | Breadth of domain coverage | Entity diversity, relationship types |
| Freshness | How up-to-date the data is | Last update timestamp, staleness |
Practical Examples:
=== "Quality Assessment" Assess the quality of your knowledge graph:
```python
from semantica import (
KGQualityAssessor,
QualityMetrics,
CompletenessMetrics,
ConsistencyMetrics
)
# Initialize quality assessor
assessor = KGQualityAssessor()
# Run comprehensive quality assessment
quality_report = assessor.assess(kg)
print("Knowledge Graph Quality Report")
print(f"Overall Score: {quality_report['overall_score']:.1%}")
# Completeness analysis
completeness = quality_report['completeness']
print(f"Completeness: {completeness['score']:.1%}")
print(f" Entities with all required fields: {completeness['complete_entities']}/{completeness['total_entities']}")
print(f" Missing fields: {completeness['missing_fields']}")
# Consistency analysis
consistency = quality_report['consistency']
print(f"Consistency: {consistency['score']:.1%}")
print(f" Conflicts detected: {consistency['conflict_count']}")
print(f" Type mismatches: {consistency['type_mismatches']}")
# Coverage analysis
coverage = quality_report['coverage']
print(f"Coverage: {coverage['score']:.1%}")
print(f" Entity types: {coverage['entity_type_count']}")
print(f" Relationship types: {coverage['relationship_type_count']}")
print(f" Orphaned entities: {coverage['orphaned_count']}")
```
=== "Validation and Constraints" Validate your graph against rules and constraints:
```python
from semantica import ValidationEngine, ConstraintValidator
from semantica.kg import GraphValidator
# Initialize validation engine
validator = ValidationEngine()
graph_validator = GraphValidator()
# Define custom validation rules
rules = [
{
"name": "entity_has_type",
"description": "Every entity must have a type",
"check": lambda e: "type" in e and e["type"]
},
{
"name": "relationship_has_valid_endpoints",
"description": "Relationships must reference existing entities",
"check": lambda r, entities: r["source"] in entities and r["target"] in entities
},
{
"name": "person_has_name",
"description": "Person entities must have a name property",
"check": lambda e: e.get("type") != "Person" or "name" in e.get("properties", {})
}
]
# Run validation with custom rules
results = validator.validate(kg, rules=rules)
print("Validation Results:")
for rule_name, result in results['rule_results'].items():
status = "PASS" if result['passed'] else "FAIL"
print(f"[{status}] {rule_name}")
if not result['passed']:
print(f" Failed: {len(result['violations'])} violations")
for v in result['violations'][:3]:
print(f" - {v['entity_id']}: {v['message']}")
# Validate graph structure
graph_result = graph_validator.validate(kg)
print(f"Graph Valid: {graph_result['valid']}")
if graph_result['errors']:
for error in graph_result['errors'][:3]:
print(f" Error: {error}")
# Check cardinality constraints
constraint_validator = ConstraintValidator()
constraint_results = constraint_validator.validate(kg, [
{"property": "CEO_OF", "max_cardinality": 1},
{"property": "WORKS_FOR", "min_cardinality": 0, "max_cardinality": 3}
])
```
=== "Automated Fixes" Automatically fix common quality issues:
```python
from semantica import AutomatedFixer, IssueTracker, KGQualityAssessor
# Track and find issues
tracker = IssueTracker()
issues = tracker.find_issues(kg)
print(f"Found {len(issues)} issues:")
for issue in issues[:5]:
print(f" [{issue['severity']}] {issue['type']}: {issue['message']}")
# Initialize fixer
fixer = AutomatedFixer()
# Apply automated fixes
fixed_kg, fix_report = fixer.fix(
kg,
fix_types=[
"missing_entity_type",
"orphaned_relationships",
"duplicate_relationships",
"empty_properties"
],
dry_run=False
)
print(f"Applied {fix_report['fixes_applied']} fixes:")
for fix_type, count in fix_report['by_type'].items():
print(f" {fix_type}: {count}")
# Measure quality improvement
assessor = KGQualityAssessor()
quality_after = assessor.assess(fixed_kg)
print(f"Quality after fixes: {quality_after['overall_score']:.1%}")
```
=== "Quality Reporting" Generate detailed quality reports:
```python
from semantica import QualityReporter, ImprovementSuggestions
# Initialize reporter
reporter = QualityReporter()
# Generate HTML report
reporter.generate_report(
kg,
output_path="quality_report.html",
format="html",
include_visualizations=True
)
# Generate JSON report for programmatic use
json_report = reporter.generate_report(kg, format="json")
print(f"Report generated with {len(json_report.get('sections', []))} sections")
# Get improvement suggestions
suggestions = ImprovementSuggestions()
recommendations = suggestions.analyze(kg)
print("Improvement Suggestions:")
for rec in recommendations:
print(f" Priority {rec['priority']}: {rec['suggestion']}")
print(f" Impact: {rec['expected_improvement']}")
print(f" Effort: {rec['effort_level']}")
```
Related Modules:
kg_qaModule - Quality assurance and evaluationconflictsModule - Conflict detection
9. Deduplication & Entity Resolution
!!! abstract "Definition" Deduplication and Entity Resolution are processes that identify and merge duplicate entities in a knowledge graph, ensuring that the same real-world entity is represented by a single node.
Why It Matters:
- Multiple sources may refer to the same entity differently
- "Apple Inc." vs "Apple" vs "Apple Computer" → Same entity
- Prevents graph fragmentation and improves query accuracy
Resolution Process:
Deduplication works by calculating similarity between entities. If similarity exceeds a threshold, entities are merged; otherwise, they remain separate.
Practical Examples:
=== "Entity Resolution" Identify and resolve duplicate entities:
```python
from semantica.kg import EntityResolver, Deduplicator, resolve_entities
# Sample entities with potential duplicates
entities = [
{"id": "e1", "text": "Apple Inc.", "type": "Organization"},
{"id": "e2", "text": "Apple", "type": "Organization"},
{"id": "e3", "text": "Apple Computer, Inc.", "type": "Organization"},
{"id": "e4", "text": "Microsoft", "type": "Organization"},
{"id": "e5", "text": "MSFT", "type": "Organization"},
{"id": "e6", "text": "Microsoft Corporation", "type": "Organization"},
]
# Method 1: Using convenience function
resolved = resolve_entities(entities, method="fuzzy")
# Method 2: Using EntityResolver class for more control
resolver = EntityResolver(
strategy="fuzzy", # "exact", "fuzzy", or "semantic"
similarity_threshold=0.85,
use_embeddings=True
)
# Find duplicate groups
duplicate_groups = resolver.find_duplicates(entities)
print("Duplicate Groups Found:")
for i, group in enumerate(duplicate_groups, 1):
print(f"Group {i}:")
for entity in group['entities']:
print(f" - {entity['text']} (id: {entity['id']})")
print(f" Confidence: {group['confidence']:.2f}")
```
=== "Semantic Matching" Use embeddings for semantic similarity matching:
```python
from semantica.kg import EntityResolver
from semantica.embeddings import EmbeddingGenerator
# Initialize with semantic matching
generator = EmbeddingGenerator()
resolver = EntityResolver(
strategy="semantic",
embedding_generator=generator,
similarity_threshold=0.8
)
# Entities with semantic variations
entities = [
{"id": "e1", "text": "Chief Executive Officer", "type": "Title"},
{"id": "e2", "text": "CEO", "type": "Title"},
{"id": "e3", "text": "Managing Director", "type": "Title"},
{"id": "e4", "text": "Head of the Company", "type": "Title"},
{"id": "e5", "text": "Software Developer", "type": "Title"},
{"id": "e6", "text": "Programmer", "type": "Title"},
]
# Find semantically similar entities
semantic_matches = resolver.find_semantic_matches(entities)
print("Semantic Matches:")
for match in semantic_matches:
print(f" {match['entity1']['text']} ~ {match['entity2']['text']}")
print(f" Similarity: {match['similarity']:.3f}")
clusters = resolver.cluster_entities(
entities,
method="semantic",
min_cluster_size=2
)
print(f"Found {len(clusters)} semantic clusters")
```
=== "Graph Deduplication" Deduplicate an entire knowledge graph:
```python
from semantica.kg import Deduplicator, deduplicate_graph
# Method 1: Using convenience function
dedup_result = deduplicate_graph(kg, method="default")
# Method 2: Using Deduplicator class for more control
deduplicator = Deduplicator(
entity_similarity_threshold=0.85,
relationship_merge_strategy="union", # "union", "intersection", "first"
preserve_provenance=True
)
# Deduplicate the knowledge graph
dedup_result = deduplicator.deduplicate(kg)
print("Deduplication Results:")
print(f" Original entities: {dedup_result['original_entity_count']}")
print(f" After dedup: {dedup_result['final_entity_count']}")
print(f" Duplicates merged: {dedup_result['merged_count']}")
print(f" Reduction: {dedup_result['reduction_percent']:.1f}%")
# Get deduplicated graph
clean_kg = dedup_result['graph']
print("Merge Details:")
for merge in dedup_result['merges'][:5]:
print(f" Merged {len(merge['source_ids'])} entities -> {merge['canonical_id']}")
print(f" Canonical: {merge['canonical_text']}")
print(f" Aliases: {', '.join(merge['aliases'])}")
```
=== "Custom Matching Rules" Define custom matching rules for your domain:
```python
from semantica.kg import EntityResolver
# Define custom matching rules
custom_rules = {
"Organization": {
"normalize": [
("Inc.", ""),
("Corp.", ""),
("Corporation", ""),
("LLC", ""),
("Ltd.", ""),
],
"abbreviations": {
"IBM": "International Business Machines",
"GE": "General Electric",
"HP": "Hewlett-Packard",
},
"threshold": 0.8
},
"Person": {
"match_fields": ["name", "email"],
"normalize_case": True,
"threshold": 0.9
},
"Location": {
"normalize": [
("St.", "Street"),
("Ave.", "Avenue"),
("Blvd.", "Boulevard"),
],
"threshold": 0.85
}
}
resolver = EntityResolver(
strategy="rule_based",
custom_rules=custom_rules
)
# Apply custom rules
entities = [
{"id": "e1", "text": "Apple Inc.", "type": "Organization"},
{"id": "e2", "text": "Apple Corporation", "type": "Organization"},
{"id": "e3", "text": "International Business Machines", "type": "Organization"},
{"id": "e4", "text": "IBM", "type": "Organization"},
]
matches = resolver.find_duplicates(entities)
print("Rule-Based Matches:")
for match in matches:
print(f" {match['entities'][0]['text']} = {match['entities'][1]['text']}")
print(f" Rule: {match['matched_rule']}")
```
=== "Merge Strategies" Control how entities are merged:
```python
from semantica.kg import EntityResolver
resolver = EntityResolver(strategy="fuzzy", similarity_threshold=0.85)
# Define merge strategy
merged_entity = resolver.merge_entities(
entities=[
{"id": "e1", "text": "Apple", "properties": {"founded": 1976}},
{"id": "e2", "text": "Apple Inc.", "properties": {"ceo": "Tim Cook"}},
{"id": "e3", "text": "Apple Computer", "properties": {"founded": 1976, "industry": "Tech"}}
],
strategy={
"id": "keep_first", # Use first entity's ID
"text": "most_complete", # Use longest/most complete text
"properties": "merge_all", # Combine all properties
"conflicts": "keep_latest" # For conflicting values, keep latest
}
)
print("Merged Entity:")
print(f" ID: {merged_entity['id']}")
print(f" Text: {merged_entity['text']}")
print(f" Properties: {merged_entity['properties']}")
print(f" Source IDs: {merged_entity['source_ids']}")
```
Related Modules:
deduplicationModule - Deduplication and mergingembeddingsModule - Similarity calculation
10. Data Normalization
!!! abstract "Definition" Data Normalization is the process of cleaning and standardizing data into a consistent format, ensuring uniformity across your knowledge graph.
Normalization Pipeline:
flowchart LR
Raw[Raw Text] --> Clean[Text Cleaning]
Clean --> Entity[Entity<br/>Normalization]
Entity --> Date[Date<br/>Normalization]
Date --> Number[Number<br/>Normalization]
Number --> Normalized[Normalized<br/>Data]
style Raw fill:#ffcdd2
style Normalized fill:#c8e6c9
Practical Examples:
=== "Text Normalization" Clean and normalize text data:
```python
from semantica.normalize import TextNormalizer
normalizer = TextNormalizer(
lowercase=True,
remove_punctuation=False,
remove_extra_whitespace=True,
unicode_normalize=True,
expand_contractions=True
)
# Normalize text
raw_texts = [
" Apple Inc. was founded in 1976!! ",
"The iPhone's really great, isn't it?",
"Microsoft™ announced Windows® 11",
"café, naïve, résumé — special chars"
]
for raw in raw_texts:
normalized = normalizer.normalize(raw)
print(f"Before: {raw!r}")
print(f"After: {normalized!r}\n")
# Batch normalization
normalized_batch = normalizer.normalize_batch(raw_texts)
```
=== "Entity Normalization" Standardize entity names and types:
```python
from semantica.normalize import EntityNormalizer
normalizer = EntityNormalizer(
case_style="title", # "title", "upper", "lower", "preserve"
remove_suffixes=["Inc.", "Corp.", "LLC", "Ltd."],
remove_prefixes=["The"],
normalize_whitespace=True
)
# Normalize entity names
entities = [
{"text": " THE apple INC. ", "type": "Organization"},
{"text": "MICROSOFT CORPORATION", "type": "Organization"},
{"text": "tim cook", "type": "Person"},
{"text": " new york city ", "type": "Location"}
]
normalized = normalizer.normalize_entities(entities)
print("Normalized Entities:")
for orig, norm in zip(entities, normalized):
print(f" {orig['text']!r} -> {norm['text']!r}")
```
=== "Date Normalization" Parse and normalize date formats:
```python
from semantica.normalize import DateNormalizer
normalizer = DateNormalizer(
output_format="ISO8601", # "ISO8601", "US", "EU", "unix"
infer_missing=True,
handle_relative=True
)
# Various date formats
dates = [
"January 15, 2024",
"15/01/2024",
"01-15-2024",
"2024-01-15",
"last Tuesday",
"3 days ago",
"Q1 2024",
"mid-2023",
]
print("Date Normalization:")
for date_str in dates:
result = normalizer.normalize(date_str)
print(f" {date_str:20} -> {result['normalized']}")
if result.get('is_range'):
print(f" Range: {result['start']} to {result['end']}")
```
=== "Number Normalization" Standardize numeric formats:
```python
from semantica.normalize import NumberNormalizer
normalizer = NumberNormalizer(
output_format="numeric", # "numeric", "text", "scientific"
handle_currencies=True,
handle_percentages=True
)
# Various number formats
numbers = [
"1,234,567",
"one million",
"$3.5 billion",
"45%",
"1.5M",
"three hundred twenty-one",
"€50,000",
"12.5 percent"
]
print("Number Normalization:")
for num_str in numbers:
result = normalizer.normalize(num_str)
print(f" {num_str:25} -> {result['value']:>15,.2f}")
if result.get('currency'):
print(f" Currency: {result['currency']}")
if result.get('is_percentage'):
print(f" Percentage: {result['decimal']:.4f}")
```
=== "Full Pipeline" Run complete normalization pipeline:
```python
from semantica.normalize import (
TextNormalizer,
EntityNormalizer,
DateNormalizer,
NumberNormalizer
)
# Initialize normalizers
text_norm = TextNormalizer(remove_extra_whitespace=True)
entity_norm = EntityNormalizer(case_style="title", remove_suffixes=["Inc.", "Corp."])
date_norm = DateNormalizer(output_format="ISO8601")
number_norm = NumberNormalizer(handle_currencies=True)
# Raw knowledge graph data
raw_kg = {
"entities": [
{"id": "e1", "text": " APPLE INC. ", "type": "Organization",
"properties": {"founded": "April 1, 1976", "revenue": "$394.3 billion"}},
{"id": "e2", "text": "tim cook", "type": "Person"},
],
"relationships": [
{"source": "e2", "target": "e1", "type": "CEO_OF",
"properties": {"since": "August 24, 2011"}}
]
}
# Normalize entities
normalized_entities = []
for entity in raw_kg['entities']:
norm_entity = entity.copy()
# Normalize text
norm_entity['text'] = text_norm.normalize(entity['text'])
norm_entity['text'] = entity_norm.normalize_entity_name(
norm_entity['text'],
entity_type=entity['type']
)
# Normalize properties
if 'properties' in entity:
props = entity['properties'].copy()
if 'founded' in props:
props['founded'] = date_norm.normalize(props['founded'])['normalized']
if 'revenue' in props:
props['revenue'] = number_norm.normalize(props['revenue'])
norm_entity['properties'] = props
normalized_entities.append(norm_entity)
# Normalize relationship properties
normalized_rels = []
for rel in raw_kg['relationships']:
norm_rel = rel.copy()
if 'properties' in rel and 'since' in rel['properties']:
norm_rel['properties']['since'] = date_norm.normalize(
rel['properties']['since']
)['normalized']
normalized_rels.append(norm_rel)
normalized_kg = {
"entities": normalized_entities,
"relationships": normalized_rels
}
print("Normalized Knowledge Graph:")
for entity in normalized_kg['entities']:
print(f" Entity: {entity['text']}")
for key, value in entity.get('properties', {}).items():
print(f" {key}: {value}")
```
Related Modules:
normalizeModule - Data normalizationparseModule - Document parsing
11. Conflict Detection
!!! abstract "Definition" Conflict Detection identifies contradictory information in a knowledge graph, such as conflicting facts about the same entity from different sources.
Conflict Types:
| Type | Description | Example |
|---|---|---|
| Value Conflict | Different values for same property | "Founded: 1976" vs "Founded: 1977" |
| Relationship Conflict | Conflicting relationships | "CEO: Tim Cook" vs "CEO: Steve Jobs" |
| Type Conflict | Different entity types | "Apple: Company" vs "Apple: Product" |
| Temporal Conflict | Conflicting time information | "Active: 2020-2023" vs "Active: 2021-2024" |
Practical Examples:
=== "Detecting Conflicts" Find conflicts in your knowledge graph:
```python
from semantica.kg import ConflictDetector
from semantica.conflicts import ConflictAnalyzer, detect_conflicts
# Sample knowledge graph with conflicts
kg = {
"entities": [
{
"id": "e1",
"text": "Apple Inc.",
"type": "Organization",
"properties": {"founded": "1976", "employees": "160000"},
"source": "wikipedia"
},
{
"id": "e2",
"text": "Apple Inc.",
"type": "Organization",
"properties": {"founded": "1977", "employees": "164000"},
"source": "bloomberg"
},
],
"relationships": [
{"source": "p1", "target": "e1", "type": "CEO_OF",
"properties": {"since": "2011"}, "data_source": "sec_filing"},
{"source": "p2", "target": "e1", "type": "CEO_OF",
"properties": {"since": "2011"}, "data_source": "news"}
]
}
# Method 1: Using convenience function
conflicts = detect_conflicts(
kg["entities"],
method="value",
property_name="founded"
)
# Method 2: Using ConflictDetector class
detector = ConflictDetector()
all_conflicts = detector.detect_value_conflicts(kg["entities"], "founded")
print(f"Found {len(all_conflicts)} conflicts:")
for conflict in all_conflicts:
print(f" Type: {conflict.conflict_type.value}")
print(f" Entity: {conflict.entity_id}")
print(f" Property: {conflict.property_name}")
print(f" Values: {[v['value'] for v in conflict.values]}")
print(f" Sources: {conflict.sources}")
# Analyze conflicts
analyzer = ConflictAnalyzer()
analysis = analyzer.analyze(all_conflicts)
print(f"Conflict Analysis:")
print(f" Total conflicts: {analysis['total_conflicts']}")
print(f" By type: {analysis['by_type']}")
print(f" By severity: {analysis['by_severity']}")
```
=== "Resolution Strategies" Resolve conflicts using different strategies:
```python
from semantica.conflicts import (
ConflictResolver,
ResolutionStrategy,
Conflict,
ConflictType
)
# Create sample conflicts
conflicts = [
Conflict(
conflict_id="c1",
entity_id="e1",
conflict_type=ConflictType.VALUE,
property_name="founded",
values=[
{"value": "1976", "source": "wikipedia", "confidence": 0.95},
{"value": "1976", "source": "sec_filing", "confidence": 0.99},
{"value": "1977", "source": "news_article", "confidence": 0.70}
],
sources=["wikipedia", "sec_filing", "news_article"]
)
]
# Strategy 1: Voting (most common value wins)
resolver_voting = ConflictResolver(strategy=ResolutionStrategy.VOTING)
resolved_voting = resolver_voting.resolve(conflicts)
print("Voting Resolution:")
for r in resolved_voting:
print(f" {r.property_name}: {r.resolved_value}")
# Strategy 2: Highest confidence
resolver_conf = ConflictResolver(strategy=ResolutionStrategy.HIGHEST_CONFIDENCE)
resolved_conf = resolver_conf.resolve(conflicts)
print("Highest Confidence Resolution:")
for r in resolved_conf:
print(f" {r.property_name}: {r.resolved_value} (confidence: {r.confidence:.2f})")
# Strategy 3: Source priority
resolver_priority = ConflictResolver(
strategy=ResolutionStrategy.SOURCE_PRIORITY,
source_priority=["sec_filing", "wikipedia", "news_article"]
)
resolved_priority = resolver_priority.resolve(conflicts)
print("Source Priority Resolution:")
for r in resolved_priority:
print(f" {r.property_name}: {r.resolved_value} (source: {r.selected_source})")
```
=== "Custom Resolution Rules" Define custom resolution rules per property:
```python
from semantica.conflicts import (
ConflictResolver,
ResolutionStrategy,
resolve_conflicts
)
# Define property-specific resolution rules
resolution_config = {
"founded": {
"strategy": ResolutionStrategy.SOURCE_PRIORITY,
"source_priority": ["official_records", "sec_filing", "wikipedia"]
},
"revenue": {
"strategy": ResolutionStrategy.MOST_RECENT,
},
"employees": {
"strategy": ResolutionStrategy.VOTING,
},
"ceo": {
"strategy": ResolutionStrategy.HIGHEST_CONFIDENCE,
}
}
# Create resolver with custom config
resolver = ConflictResolver(
strategy=ResolutionStrategy.HIGHEST_CONFIDENCE,
min_confidence=0.7
)
# Resolve conflicts for each property
resolved_values = {}
for property_name, config in resolution_config.items():
property_resolver = ConflictResolver(
strategy=config["strategy"],
source_priority=config.get("source_priority")
)
# Get conflicts for this property
property_conflicts = [c for c in conflicts if c.property_name == property_name]
if property_conflicts:
results = property_resolver.resolve(property_conflicts)
for result in results:
resolved_values[property_name] = {
"value": result.resolved_value,
"strategy": config["strategy"].value,
"confidence": result.confidence
}
print("Resolved Values:")
for prop, info in resolved_values.items():
print(f" {prop}: {info['value']} (via {info['strategy']})")
```
=== "Source Tracking" Track the origin of resolved values:
```python
from semantica.conflicts import SourceTracker, SourceReference
# Initialize source tracker
tracker = SourceTracker()
# Add entities from different sources
tracker.add_source(SourceReference(
entity_id="e1",
property_name="founded",
value="1976",
source="wikipedia",
timestamp="2024-01-15",
confidence=0.95
))
tracker.add_source(SourceReference(
entity_id="e1",
property_name="founded",
value="1976",
source="sec_filing",
timestamp="2024-03-01",
confidence=0.99
))
tracker.add_source(SourceReference(
entity_id="e1",
property_name="founded",
value="1977",
source="news_article",
timestamp="2024-02-20",
confidence=0.70
))
# Get all sources for an entity
sources = tracker.get_sources("e1")
print("Entity Sources:")
for source in sources:
print(f" Source: {source.source}")
print(f" Property: {source.property_name}")
print(f" Value: {source.value}")
print(f" Confidence: {source.confidence}")
# Get property-specific sources
property_sources = tracker.get_property_sources("e1", "founded")
print("Property Sources for 'founded':")
for ps in property_sources:
print(f" {ps.value} from {ps.source} ({ps.timestamp})")
```
=== "Conflict Investigation" Deep dive into specific conflicts:
```python
from semantica.conflicts import (
InvestigationGuideGenerator,
ConflictDetector,
Conflict,
ConflictType
)
# Initialize investigation guide generator
guide_generator = InvestigationGuideGenerator()
# Create a conflict object
conflict = Conflict(
conflict_id="c1",
entity_id="e1",
conflict_type=ConflictType.VALUE,
property_name="founded",
values=[
{"value": "1976", "source": "wikipedia", "confidence": 0.95},
{"value": "1977", "source": "news", "confidence": 0.70}
],
sources=["wikipedia", "news"],
severity=0.6
)
# Generate investigation guide
guide = guide_generator.generate_guide(conflict)
print("Conflict Investigation Guide:")
print(f" Conflict ID: {guide.conflict_id}")
print(f" Title: {guide.title}")
print(f" Summary: {guide.summary}")
print("Investigation Steps:")
for i, step in enumerate(guide.steps, 1):
print(f" {i}. {step.description}")
print(f" Priority: {step.priority}")
if step.expected_outcome:
print(f" Expected: {step.expected_outcome}")
print("Checklist:")
for item in guide.checklist:
print(f" [ ] {item}")
# Get context for the conflict
context = guide_generator.get_conflict_context(conflict)
print(f"Context:")
print(f" Entity: {context.get('entity_text', 'Unknown')}")
print(f" Property: {context.get('property', 'Unknown')}")
print(f" Value count: {len(conflict.values)}")
```
Related Modules:
conflictsModule - Conflict detection and resolutionkg_qaModule - Quality assurance
Comparison Tables
Embedding Providers Comparison
| Provider | Model | Dimensions | Speed | Cost | Accuracy | Best For |
|---|---|---|---|---|---|---|
| OpenAI | text-embedding-3-large | 3072 | Fast | Paid | High | Production, high accuracy |
| OpenAI | text-embedding-3-small | 1536 | Fast | Paid | High | Balanced performance |
| Cohere | embed-english-v3.0 | 1024 | Fast | Paid | High | Multilingual support |
| HuggingFace | all-MiniLM-L6-v2 | 384 | Medium | Free | Medium | Development, open source |
| Local | sentence-transformers | 384-768 | Slow | Free | Medium | Privacy, offline use |
Graph Backend Comparison
| Backend | Type | Speed | Scalability | Query Language | Best For |
|---|---|---|---|---|---|
| NetworkX | In-memory | Fast | Small-medium | Python API | Development, small graphs |
| Neo4j | Database | Medium | Large | Cypher | Production, complex queries |
| KuzuDB | Embedded | Fast | Medium | Cypher | Embedded applications |
| FalkorDB | Redis-based | Very Fast | Large | Cypher | Real-time, high throughput |
Best Practices
Following these practices will help you build high-quality knowledge graphs and avoid common pitfalls.
1. Start Small
!!! tip "Iterative Approach" Don't try to model the entire world at once. Start with a small, well-defined domain and expand incrementally.
Example:
# Start with a single document
kg1 = semantica.build_knowledge_base(["doc1.pdf"])
# Validate and refine
quality = assessor.assess(kg1)
# Then expand
kg2 = semantica.build_knowledge_base(["doc2.pdf", "doc3.pdf"])
merged = semantica.kg.merge([kg1, kg2])
2. Configure Properly
- Use environment variables for sensitive data
- Set up proper logging
- Configure appropriate model sizes
- Use configuration files for complex setups
# Good: Use environment variables
import os
api_key = os.getenv("OPENAI_API_KEY")
# Good: Use config files
from semantica import Config
config = Config.from_file("config.yaml")
semantica = Semantica(config=config)
3. Validate Data
!!! warning "Garbage In, Garbage Out" Always validate extracted entities. A knowledge graph with incorrect facts is worse than no graph at all.
Validation Checklist:
- Check entity extraction accuracy
- Validate relationships make sense
- Verify confidence scores
- Review source attribution
- Test with sample queries
4. Handle Errors
- Implement error handling
- Use retry mechanisms
- Log errors for debugging
- Gracefully handle API failures
from semantica import Semantica
import logging
logging.basicConfig(level=logging.INFO)
semantica = Semantica()
try:
result = semantica.build_knowledge_base(["doc.pdf"])
except Exception as e:
logging.error(f"Error building KG: {e}")
# Handle error appropriately
5. Optimize Performance
- Use batch processing for large datasets
- Enable parallel processing where possible
- Cache embeddings and results
- Use appropriate backend for your scale
# Batch processing
sources = ["doc1.pdf", "doc2.pdf", ..., "doc100.pdf"]
batch_size = 10
for i in range(0, len(sources), batch_size):
batch = sources[i:i+batch_size]
result = semantica.build_knowledge_base(batch)
# Process and save results
6. Document Workflows
- Document data sources
- Track processing steps
- Maintain metadata
- Version your knowledge graphs
Next Steps
Now that you understand the core concepts:
- Getting Started - Set up Semantica and build your first knowledge graph
- Modules Guide - Learn about the available modules
- Use Cases - Explore real-world applications
- Examples - See practical code examples
- API Reference - Detailed API documentation
!!! info "Contribute" Found an issue or want to improve this guide? Contribute on GitHub
Last Updated: 2024