A knowledge graph represents information as entities connected by named relationships, such as a customer owning an account or a product depending on a component. It makes those connections explicit and queryable, giving AI systems structured context that is difficult to recover reliably from isolated text passages.
Microsoft’s GraphRAG indexing documentation shows one way to extract entities, relationships, and claims from unstructured documents before building a graph.
How does a knowledge graph help an AI system?
It lets retrieval follow explicit links between people, products, policies, events, or other domain objects. Relationships become data that can be queried and checked, rather than associations the model must infer again from prose. Graph RAG can combine those structures with generated answers.
What makes a knowledge graph trustworthy?
Entity resolution, provenance, update ownership, and validation matter more than graph size. Structured output can help extraction, but it cannot prove that a relationship is true. Keep links to their source records and apply AI governance to changes. Embeddings and ordinary RAG remain useful for text that does not need explicit graph relationships.
Frequently asked questions
How is a knowledge graph different from a database?
A knowledge graph is a data model centered on entities and relationships. It can be stored in a graph database, relational database, or another system that preserves those semantics.
Does a knowledge graph replace vector search?
No. Graph queries are strong at explicit relationships, while vector search is strong at semantic similarity. Many retrieval systems use both.
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