Knowledge Graph Builder

Model entity relationships by designing an ontology that defines entities, their properties, and how they connect. Use a graph database like Neo4j to store nodes (entities) and edges (relationships), enabling semantic queries and traversal across interconnected data structures.

Hybrid search combines graph traversal for relationship discovery with vector embeddings for semantic matching. Query the graph for connected entities, then rank results using vector similarity to find contextually relevant relationships alongside structural ones.

Extract entities and relationships from source data, then validate them against your ontology schema to ensure they conform to defined entity types and relationship constraints. This maintains data integrity and enables reliable fact verification in downstream queries.

Yes. Ground AI systems by connecting them to a knowledge graph through RAG workflows. The graph provides structured, verifiable facts that reduce hallucinations and enable the AI to reason about entity relationships and constraints during generation.

Define entity types, relationship types, properties, and constraints specific to your domain before implementation. Ontology design shapes query patterns, scalability, and how effectively the graph captures domain semantics for retrieval and reasoning.

Yes, when paired with a scalable graph database and optimized query patterns. Performance depends on ontology design, indexing strategy, and query complexity, but knowledge graphs handle large interconnected datasets efficiently for semantic search and relationship traversal.