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KaifAhmad1
01791562f1
- Remove ConflictDetector and Deduplicator from semantica.kg module - Update all imports to use semantica.conflicts and semantica.deduplication - Update all notebooks to use class-based API (no convenience functions) - Fix method signatures: pass graph parameter to methods instead of constructor - Update calculate_centrality calls to use specific methods (calculate_degree_centrality, etc.) - Fix detect_communities and analyze_connectivity return value handling - Update all documentation (kg_usage.md, docs/reference/kg.md) - Remove conflict_detector.py and deduplicator.py from kg module - Update registry.py to remove conflict and deduplicate task types
5.3 KiB
5.3 KiB
Graph Analytics
Overview
This notebook demonstrates how to analyze knowledge graphs using Semantica's analytics modules. You'll learn to use GraphAnalyzer, CentralityCalculator, CommunityDetector, and ConnectivityAnalyzer to understand graph structure and properties.
Documentation: API Reference
Learning Objectives
- Use
GraphAnalyzerfor comprehensive graph analysis - Use
CentralityCalculatorto compute centrality measures - Use
CommunityDetectorto find communities in graphs - Use
ConnectivityAnalyzerto analyze graph connectivity
Installation
Install Semantica from PyPI:
pip install semantica
# Or with all optional dependencies:
pip install semantica[all]
Step 1: Graph Analysis
Analyze graph structure and properties.
In [ ]:
from semantica.kg import GraphBuilder, GraphAnalyzer
from semantica.semantic_extract import NERExtractor, RelationExtractor
builder = GraphBuilder()
analyzer = GraphAnalyzer()
entities = [
{"id": "e1", "type": "Organization", "name": "Apple Inc.", "properties": {}},
{"id": "e2", "type": "Person", "name": "Tim Cook", "properties": {}},
{"id": "e3", "type": "Location", "name": "Cupertino", "properties": {}}
]
relationships = [
{"source": "e2", "target": "e1", "type": "CEO_of", "properties": {}},
{"source": "e1", "target": "e3", "type": "located_in", "properties": {}}
]
kg = builder.build(entities, relationships)
metrics = analyzer.compute_metrics(kg)
print(f"Graph metrics:")
print(f" Entities: {metrics.get('entity_count', 0)}")
print(f" Relationships: {metrics.get('relationship_count', 0)}")
print(f" Density: {metrics.get('density', 0):.3f}")
In [ ]:
from semantica.kg import CentralityCalculator
centrality_calculator = CentralityCalculator()
centrality_result = centrality_calculator.calculate_degree_centrality(kg)
centrality_scores = centrality_result.get('centrality', {})
print(f"Centrality scores:")
for entity_id, score in list(centrality_scores.items())[:5]:
print(f" {entity_id}: {score:.3f}")
In [ ]:
from semantica.kg import CommunityDetector
community_detector = CommunityDetector()
communities = community_detector.detect_communities(kg)
print(f"Detected {len(communities)} communities")
for i, community in enumerate(communities[:3], 1):
print(f" Community {i}: {len(community)} entities")
In [ ]:
from semantica.kg import ConnectivityAnalyzer
connectivity_analyzer = ConnectivityAnalyzer()
connectivity = connectivity_analyzer.analyze_connectivity(kg)
print(f"Connectivity analysis:")
print(f" Is connected: {connectivity.get('is_connected', False)}")
print(f" Components: {len(connectivity.get('components', []))}")