Knowledge Graph Builder

Build structured knowledge graphs for enhanced AI system performance through relational knowledge. Knowledge graphs make implicit relationships explicit , enabling AI systems to reason about connections, verify facts, and avoid hallucinations. Goal : Define entities, relationships, and properties for your domain Entity Types (Nodes): Relationship Types (Edges): Properties (Attributes): Example Ontology : Validation : Decision Matrix : Neo4j (Recommended for most): Amazon Neptune : ArangoDB : TigerGraph : Technology Stack : Neo4j Schema Setup : Goal : Extract entities and relationships from data sources Data Sources : Entity Extraction Pipeline : Relationship Extraction : LLM-Based Extraction (for complex relationships): Validation : Goal : Combine structured graph with semantic vector search Architecture : Benefits of Hybrid Approach : Common Query Patterns : 1. Find Entity : 2. Find Relationships : 3. Path Between Entities : 4. Multi-Hop Traversal : 5. Recommendation Query : Knowledge Graph API : Goal : Use knowledge graph to ground LLM responses and detect hallucinations Knowledge Graph RAG : Hallucination Detection : Define your schema before ingesting data.