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Replace Hawksight-AI/semantica, semantica-dev/semantica, and semantica/semantica URLs across all docs files (17 files, ~100 links).
234 lines
7.7 KiB
Markdown
234 lines
7.7 KiB
Markdown
---
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title: "Knowledge Graph Module"
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description: "Graph construction, temporal models, analytics, and distance intelligence."
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icon: "diagram-project"
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---
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> High-level KG construction, management, and analysis system.
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---
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## Overview
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The **Knowledge Graph (KG) Module** transforms extracted entities and relationships into structured, queryable knowledge graphs. It includes temporal support, graph analytics, node embeddings, and distance intelligence (v0.5.0).
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<CardGroup cols={2}>
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<Card title="KG Construction" icon="hammer">
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Build graphs from entities and relationships with automatic entity merging.
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</Card>
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<Card title="Temporal Graphs" icon="clock">
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Time-aware edges (`valid_from`, `valid_until`) and point-in-time queries (v0.4.0).
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</Card>
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<Card title="Graph Analytics" icon="chart-bar">
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Centrality, community detection, and connectivity analysis.
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</Card>
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<Card title="Distance Intelligence" icon="compass">
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Semantic neighborhoods, distance matrices, and ego-mode exploration (v0.5.0).
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</Card>
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<Card title="Provenance" icon="link">
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Track the source and lineage of every node and edge.
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</Card>
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<Card title="Entity Resolution" icon="user-check">
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Resolve and merge similar entities using fuzzy matching and semantic similarity.
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</Card>
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</CardGroup>
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<Tip>
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**Related modules:** Use `semantica.conflicts` for conflict detection and `semantica.deduplication` for advanced entity resolution.
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</Tip>
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---
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## GraphBuilder
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Constructs knowledge graphs from raw entities and relationships.
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```python
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from semantica.kg import GraphBuilder
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builder = GraphBuilder(merge_entities=True)
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kg = builder.build(entities=entities, relationships=relationships)
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```
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| Method | Description |
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|--------|-------------|
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| `build(sources)` | Build graph from multiple data sources |
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| `build_single_source(data)` | Build graph from single data source |
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| `merge_entities()` | Merge duplicate entities during building |
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---
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## Temporal Knowledge Graphs (v0.4.0)
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```python
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from semantica.kg import TemporalKnowledgeGraph, TemporalGraphQuery
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from datetime import datetime
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# Build temporal graph
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tkg = TemporalKnowledgeGraph()
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tkg.add_node("ceo_role", valid_from=datetime(2020, 1, 1), valid_until=datetime(2023, 6, 1))
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tkg.add_edge("alice", "acme_corp", "ceo_of",
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valid_from=datetime(2020, 1, 1), valid_until=datetime(2023, 6, 1))
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# Point-in-time query
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snapshot = tkg.at(datetime(2021, 6, 15))
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# Query evolution
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query = TemporalGraphQuery(tkg)
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snapshot_2020 = query.at_time("2020-01-01")
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snapshot_2023 = query.at_time("2023-01-01")
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diff = snapshot_2023.minus(snapshot_2020)
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print(f"New nodes since 2020: {len(diff.nodes)}")
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```
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Supports all 13 Allen interval algebra relations (before, after, meets, overlaps, during, starts, finishes, equals, and their inverses). OWL-Time export available.
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---
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## Distance Intelligence (v0.5.0)
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```python
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from semantica.kg import DistanceCalculator
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calc = DistanceCalculator(kg)
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# Semantic neighborhood of a node
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neighborhood = calc.semantic_neighborhood("Apple Inc.", radius=0.4)
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# N×N distance matrix
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matrix = calc.distance_matrix(["Apple Inc.", "Google", "Microsoft"])
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# Distance band classification: "near" | "mid" | "far"
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bands = calc.classify_bands(neighborhood)
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```
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---
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## Graph Analytics
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### Centrality Analysis
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```python
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from semantica.kg import CentralityCalculator
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calculator = CentralityCalculator()
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centrality = calculator.calculate_degree_centrality(graph)
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pagerank_scores = calculator.calculate_pagerank(graph, damping_factor=0.85)
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top_nodes = calculator.get_top_nodes(centrality, top_k=10)
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```
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| Method | Algorithm |
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|--------|-----------|
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| `calculate_degree_centrality()` | Degree-based importance |
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| `calculate_betweenness_centrality()` | Bridge-based importance |
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| `calculate_closeness_centrality()` | Distance-based importance |
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| `calculate_eigenvector_centrality()` | Influence-based importance |
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| `calculate_pagerank()` | PageRank scores |
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| `calculate_all_centrality()` | All measures at once |
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### Community Detection
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```python
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from semantica.kg import CommunityDetector
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detector = CommunityDetector()
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communities = detector.detect_communities(graph, algorithm="louvain")
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metrics = detector.calculate_community_metrics(graph, communities)
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leiden_communities = detector.detect_communities_leiden(graph, resolution=1.2)
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```
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Algorithms: Louvain, Leiden, Label Propagation, K-Clique Communities.
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### Path Finding
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```python
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from semantica.kg import PathFinder
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finder = PathFinder()
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path = finder.dijkstra_shortest_path(graph, "node_a", "node_b")
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paths = finder.all_shortest_paths(graph, "source", "target")
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k_paths = finder.find_k_shortest_paths(graph, "source", "target", k=3)
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```
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Algorithms: Dijkstra, A\*, BFS, All Shortest Paths, K-Shortest Paths.
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### Link Prediction
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```python
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from semantica.kg import LinkPredictor
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predictor = LinkPredictor(method="preferential_attachment")
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links = predictor.predict_links(graph, top_k=20)
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score = predictor.score_link(graph, "node_a", "node_b")
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```
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Algorithms: Preferential Attachment, Common Neighbors, Jaccard, Adamic-Adar, Resource Allocation.
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### Node Embeddings
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```python
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from semantica.kg import NodeEmbedder
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embedder = NodeEmbedder(method="node2vec", embedding_dimension=128)
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embeddings = embedder.compute_embeddings(graph_store, ["Entity"], ["RELATED_TO"])
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similar_nodes = embedder.find_similar_nodes(graph_store, "entity_123", top_k=10)
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```
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Algorithms: Node2Vec, DeepWalk, Word2Vec.
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---
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## Algorithm Summary
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| Category | Algorithms | Use Cases |
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|----------|------------|-----------|
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| Node Embeddings | Node2Vec, DeepWalk, Word2Vec | Structural similarity, node representation |
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| Similarity Analysis | Cosine, Euclidean, Manhattan, Correlation | Node similarity, recommendation |
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| Path Finding | Dijkstra, A\*, BFS, K-Shortest | Route planning, network analysis |
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| Link Prediction | Preferential Attachment, Jaccard, Adamic-Adar | Network completion |
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| Centrality | Degree, Betweenness, Closeness, PageRank | Influence analysis |
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| Community Detection | Louvain, Leiden, Label Propagation | Social analysis, clustering |
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| Connectivity | Components, Bridges, Density | Network robustness |
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---
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## Configuration
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```yaml
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kg:
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resolution:
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threshold: 0.9
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strategy: semantic
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temporal:
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enabled: true
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default_validity: infinite
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```
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---
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## See Also
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<CardGroup cols={2}>
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<Card title="Graph Store" icon="server" href="graph_store">
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Persistence layer (Neo4j, FalkorDB, Apache AGE).
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</Card>
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<Card title="Semantic Extract" icon="magnifying-glass" href="semantic_extract">
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Data source for entities and relationships.
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</Card>
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<Card title="Visualization" icon="chart-bar" href="visualization">
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Visualize knowledge graphs interactively.
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</Card>
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<Card title="Conflicts" icon="triangle-exclamation" href="conflicts">
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Conflict detection and resolution.
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</Card>
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</CardGroup>
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### Cookbook
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- [Building Knowledge Graphs](https://github.com/semantica-agi/semantica/blob/main/cookbook/introduction/07_Building_Knowledge_Graphs.ipynb) — fundamentals of KG construction · Beginner
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- [Your First Knowledge Graph](https://github.com/semantica-agi/semantica/blob/main/cookbook/introduction/08_Your_First_Knowledge_Graph.ipynb) — entity extraction to visualization · Beginner
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- [Graph Analytics](https://github.com/semantica-agi/semantica/blob/main/cookbook/introduction/10_Graph_Analytics.ipynb) — centrality and community detection · Intermediate
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- [Advanced Graph Analytics](https://github.com/semantica-agi/semantica/blob/main/cookbook/advanced/02_Advanced_Graph_Analytics.ipynb) — PageRank, Louvain, shortest path · Advanced
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- [Temporal Knowledge Graphs](https://github.com/semantica-agi/semantica/blob/main/cookbook/advanced/10_Temporal_Knowledge_Graphs.ipynb) — temporal logic and graph evolution · Advanced
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