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When should you use a knowledge graph for AI?

Use a knowledge graph when the important part of an answer is how entities relate: ownership, dependencies, history, eligibility, or multi-step paths. Use simpler retrieval when the job is mostly finding relevant passages.

For teams choosing between documents, vectors, relational data, and graph-based context. · Reviewed 2026-09-29

A graph-worthy question

“Which customer accounts use a service that depends on this failing component and are covered by the affected contract?” is a relationship question. A graph can make those links explicit. “What does this policy say about refunds?” is usually a document-retrieval question.

Use a graph when

  • The same entities appear across many systems.
  • Questions require multiple relationship hops.
  • Ownership, lineage, dependencies, or causality matter.
  • You need explainable paths rather than only similar passages.
  • The relationships are stable enough to model and validate.

Do not start with a graph when

You have a small document set, no repeated relationship questions, or no owner for maintaining the ontology. A graph can become an expensive data-modeling project without improving the user’s actual answer.

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