Knowledge Graph Builder

To build a knowledge graph for AI grounding, define a domain ontology, extract entities and relationships with confidence scoring, and validate claims against the graph evidence to detect inconsistencies and prevent hallucinations.

Hybrid KG+RAG integration combines structured graph traversal with vector search to enable semantic retrieval. This approach validates retrieved relationships against an ontology to provide confidence-scored evidence for AI reasoning.

Design an ontology by defining domain entities, relationships, and properties. Automate entity and relationship extraction to discover graph nodes, validating them against the ontology schema and assigning confidence scores to mapped relationships.

Choose a graph database by evaluating storage and querying strategies for your domain schema. You can query complex entity relationships by exposing APIs to find entities, map relationships, and calculate shortest paths across the graph.

You can bootstrap a Neo4j graph with a minimal ontology, a few entities, and mapped relationships to test queries. Graph database selection involves choosing appropriate storage and querying strategies plus implementing data governance.

Entity resolution during relationship extraction automates the discovery and validation of entities against a defined ontology. It assigns confidence scores to relationships to ensure graph accuracy and support cross-domain AI grounding.