What does this article cover?

When to use knowledge graphs with RAG, and how graph retrieval can improve precision and traceability.

Data and engineering teams building RAG over complex domains with entities, relationships and conflicting sources.

Knowledge Graphs for RAG: Entity Linking, Graph Retrieval and Traceability

RAG works well when the right evidence is easy to retrieve from text. It struggles when the domain is relationship-heavy: customers to contracts, assets to locations, policies to exceptions, people to roles. In these domains, a knowledge graph can raise precision and make evidence more traceable.

When a graph helps

A knowledge graph is most valuable when you have:

Entity ambiguity. Many things share the same name (projects, products, people).
Relationship queries. The answer depends on joins across entities and constraints.
Traceability requirements. You need to show not just documents, but the entity path that justifies an answer.

Graph retrieval patterns that work

Common patterns combine graphs and text:

Entity-first retrieval. Resolve entities (customer, asset, system) then retrieve text scoped to those entities.
Path-based evidence. Retrieve a relationship path (A -> B -> C) and attach related text snippets.
Graph-constrained search. Use graph filters as metadata constraints (see metadata strategy).

This reduces irrelevant retrieval and improves answer consistency (see ranking and relevance).

Build the ingestion pipeline deliberately

Graphs are not magic. They require disciplined ingestion:

Define stable entity identifiers and canonical sources (see canonical sources).
Capture ownership, effective dates and provenance for edges and attributes.
Version your extraction logic and keep backfills manageable (see ingestion pipelines and freshness architecture).

Permissions still apply

Graph-based retrieval can leak information through relationships. Treat permissions as part of both node and edge retrieval. Apply tenancy and ACL constraints early (see RAG permissions).

Measure the impact

Evaluate graph-assisted RAG using the same layered approach as other retrieval improvements:

Retrieval precision/recall. Do the right entities and sources appear?
Grounding. Are claims supported by retrieved evidence (see grounding)?
User outcomes. Fewer escalations, faster task completion (see value metrics).

Use golden queries and synthetic monitoring so the graph layer does not silently drift (see synthetic monitoring).

When the domain is relationship-heavy, graphs can be the difference between plausible answers and precise, traceable ones.