A concrete example
A document search can find a policy mentioning a product. A knowledge graph can represent that Product A is owned by Team B, depends on Service C, changed in Release D, and is covered by Policy E. When an incident happens, those relationships help answer “what else is affected?” rather than only “which document mentions this phrase?”
What a graph contains
- Entities such as products, people, systems, documents, or organizations.
- Relationships such as owns, depends on, replaced, applies to, or approved by.
- Properties such as dates, identifiers, status, and permissions.
- Provenance showing where a fact came from and when it was checked.
Why graphs can help AI
Graphs make relationships explicit and can support multi-hop questions. They do not automatically make the data correct: an incorrect relationship is still an incorrect fact, so validation and provenance matter.
Graph versus vector search
| Need | Better starting point |
|---|---|
| Find similar wording | Vector search |
| Follow ownership or dependency relationships | Knowledge graph |
| Answer from changing document passages | RAG |
| Do both | Hybrid graph plus retrieval architecture |