knowledge-graph-builder

To turn unstructured data into a queryable knowledge graph, you can run a complete pipeline covering domain scoping, entity extraction, disambiguation, relationship mapping, and graph construction to structure your information for multi-hop reasoning.

Yes, you can construct and query knowledge graphs using Neo4j and RDF stores. The process supports graph data store integration, allowing you to map extracted entities and relationships directly into your chosen graph database for advanced querying.

Graph-augmented reasoning enables complex multi-hop queries over connected data, supporting RAG integration by mapping relationships across entities to provide highly contextualized, structured context for retrieval-augmented generation workflows.

Yes, ontology design for domain modeling is fully supported, allowing you to create OWL/RDF-ready schemas. This ensures your knowledge graph maintains a structured, semantically consistent hierarchy for accurate entity resolution and relationship mapping.

The best way to extract entities and map relationships is through a structured pipeline that performs entity disambiguation and relationship mapping. This ensures accurate graph construction by resolving duplicate entities and defining their connections across your document set.