Knowledge graphs are back. They never left.
How knowledge graphs make AI systems more reliable — entity resolution, graph-grounded retrieval (GraphRAG), explainability and when a graph is worth it.
What a knowledge graph is, in plain terms
Think of a map of your organisation's facts: customers, products, contracts, suppliers, regulations — and the lines between them, such as 'supplies', 'is governed by' or 'replaced'. Each fact is stored once and connected to everything it relates to.
An ontology or schema defines which kinds of entities and relationships are allowed. That structure is what lets software reason over the data — for example, finding every product affected by a supplier's recall in two hops.
Why graphs matter more with language models
Language models are fluent but have no built-in notion of which facts are verified. Vector search retrieves passages that look similar to the question, which works well for single-fact lookups but struggles when the answer requires joining facts spread across many documents.
Graph-grounded retrieval, often called GraphRAG, retrieves connected entities and relationships, sometimes together with text passages and community summaries. The model then answers from a structured, inspectable context, and the system can show the path it followed.
Vector search, graphs or both?
The approaches are complementary. Many production systems use vector search to find entry points and the graph to expand and verify.
| Question type | Better fit | Why |
|---|---|---|
| 'What does our policy say about X?' | Vector search over documents | Single-passage answers; quick to set up. |
| 'Which customers are affected if supplier Y fails?' | Knowledge graph traversal | Requires following several relationships reliably. |
| 'Summarise risks across this portfolio' | Graph plus retrieval | Needs both structure and supporting text for context. |
The hard parts
The graph database is rarely the difficult part. The work is in the data.
- Entity resolution — recognising that 'ACME Ltd', 'Acme Limited' and a supplier ID are the same organisation.
- Ontology scope — modelling just enough for the questions that matter, rather than the whole world.
- Keeping it current — automated extraction pipelines with review steps, not one-off manual builds.
- Provenance — recording the source document and date for every fact so answers can be audited.
When a graph is worth the investment
A graph pays off when questions depend on relationships, when answers must be explainable, or when several systems describe the same entities differently. If most questions are answered by a single document, start with well-evaluated retrieval and add a graph only when evaluation shows multi-hop failures.
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