Entity Extraction and Graph Construction Strategies

Building effective Knowledge Graphs is crucial for AI agent performance. Learn the strategies that will optimize your graph structure for superior reasoning and contextual grounding.

Why Graph Construction Matters

Unlike traditional RAG systems that rely on simple text chunking and vector search, TrustGraph builds interconnected Knowledge Graphs. The right graph construction strategy directly impacts:

• Reasoning quality: Better graph structure = superior multi-hop reasoning
• Contextual grounding: Connected entities provide comprehensive context
• Hallucination prevention: Graph constraints prevent fabricated information
• Transparency: Traceable relationships enable explainable AI

Graph Construction Methods

1. Entity-Centric Graphs

Extract entities as nodes and relationships as edges.

await client.buildGraph({
  extraction: "entity-centric",
  entityTypes: ["person", "organization", "concept", "event"],
  linkStrategies: ["coreference", "semantic", "temporal"],
  confidence: 0.8, // Minimum confidence for entity extraction
});

Pros: Clear, interpretable graph structure Cons: Requires robust entity recognition Best for: Structured content, knowledge bases

2. Hierarchical Graphs

Build parent-child relationships for document structure.

await client.buildGraph({
  extraction: "hierarchical",
  levels: ["document", "section", "paragraph", "sentence"],
  preserveStructure: true,
  crossLevelLinks: true, // Enable semantic links across levels
});

Pros: Maintains document organization Cons: May miss semantic relationships across hierarchy Best for: Technical documentation, reports

3. Semantic Relationship Graphs

Focus on meaning-based connections rather than structure.

await client.buildGraph({
  extraction: "semantic",
  relationships: ["similarity", "causation", "temporal", "dependency"],
  embeddingModel: "all-MiniLM-L6-v2",
  similarityThreshold: 0.75,
});

Pros: Captures meaning and context Cons: More computationally intensive Best for: Unstructured text, conversational data

Recommended Strategies by Content Type

Key Parameters

Entity Extraction Confidence

Balance between recall and precision:

• Too low (< 0.6): Many false positives, noisy graph
• Too high (> 0.9): Missing entities, sparse graph
• Sweet spot: 0.75-0.85 for most content

Graph Density

Control the number of relationships per node:

{
  maxRelationshipsPerNode: 10,  // Prevent over-connected nodes
  minNodeDegree: 2,              // Ensure all nodes are connected
  pruneIsolated: true,           // Remove disconnected nodes
}

Advanced Techniques

1. Multi-Layer Graph Construction

Build graphs at different abstraction levels:

await client.buildMultiLayerGraph({
  layers: [
    { name: "atomic", granularity: "sentence", entities: "all" },
    { name: "conceptual", granularity: "paragraph", entities: "concepts" },
    { name: "document", granularity: "section", entities: "topics" },
  ],
  crossLayerLinks: true, // Enable relationships across layers
});

2. Context-Enriched Nodes

Add metadata and provenance to graph nodes:

{
  entity: "Machine Learning",
  type: "concept",
  metadata: {
    source: "document.pdf",
    section: "Chapter 3",
    page: 42,
    firstMention: "2025-12-24",
    confidence: 0.92,
  },
  attributes: {
    definition: "...",
    aliases: ["ML", "Statistical Learning"],
  }
}

3. Adaptive Graph Pruning

Dynamically refine graph quality:

await client.pruneGraph({
  removeIsolated: true,
  mergeHighSimilarity: 0.95,  // Merge near-duplicate nodes
  removeWeakLinks: 0.3,       // Remove low-confidence relationships
  consolidateEntities: true,  // Resolve entity mentions
});

Testing Your Strategy

Evaluate graph construction effectiveness:

1. Graph metrics: Measure connectivity, centrality, clustering
2. Reasoning quality: Test multi-hop query accuracy
3. Entity coverage: Ensure key entities are captured
4. Performance: Monitor graph traversal latency
5. Hallucination rate: Track grounding accuracy

Common Pitfalls

• Over-extraction: Too many low-confidence entities create noise
• Under-linking: Sparse graphs miss important relationships
• Ignoring provenance: Can't trace back to source documents
• No entity resolution: Duplicate nodes for same real-world entities
• Static graphs: Not updating as new information arrives

Comparison to Traditional RAG

Conclusion

Building effective Knowledge Graphs is fundamentally different from traditional text chunking. TrustGraph's graph-based approach enables superior reasoning, contextual grounding, and hallucination prevention. Start with entity-centric extraction at 0.8 confidence, then refine based on your domain and use case.