How to use structured knowledge — ontologies, property graphs, and knowledge graphs — alongside vector search to improve retrieval precision and answer quality.
What you will find here
- Knowledge graph basics — nodes, edges, properties, and how graph structure differs from flat document retrieval.
- GraphRAG — retrieval patterns that traverse graph relationships before generating answers, when GraphRAG beats naive RAG, and when it does not.
- Ontology integration — mapping domain concepts to structured schemas and using ontologies to constrain or expand search.
- Hybrid retrieval — combining vector similarity, graph traversal, and keyword search in a single pipeline.
- Graph databases — comparison of graph stores relevant to AI retrieval workloads and how to choose between them.
This section is particularly useful for domains with complex entity relationships: healthcare, legal, supply chain, and financial services.