Knowledge graphs in RAG

A knowledge graph in RAG uses a domain ontology and validated relationships as a governed knowledge layer without replacing primary documents. It is a separate contract with measurable inputs, outputs, and failure conditions. The team first defines query types, available sources, required evidence, and acceptable latency, and only then chooses a model or library.

Because the graph represents explicit relationships between entities, retrieval should return not only text but also the relation type and evidence of its origin. The generator must not turn a weak or indirect edge into a categorical fact. This matters especially for dependencies, ownership, compatibility, and causal claims.

A practical flow is: an entity linker maps the query to canonical nodes, traversal finds allowed paths, and text retrieval adds cited evidence for the discovered claims. The contract should contain a stable request ID, configuration versions, confidence or a fallback reason, and enough provenance to reproduce the result. Text without source identifiers is not a complete retrieval output.

A useful knowledge-graph RAG contract includes canonical entity ID, relation type, source document, validFrom, validUntil, and confidence. The text index may be rebuilt frequently, but these identities must remain reproducible so entity resolution, relation retrieval, and the final evidence bundle can be tested independently.

Build a knowledge graph when a stable schema and relationships have real business value; do not create a complex ontology for a corpus of independent reference pages. Keep the strongest simple baseline alongside it, because added complexity is justified only by measured improvement. A router can account for query type, risk, expected evidence count, and latency budget.

If an entity has several incompatible relation paths, the system must not silently choose the most convenient one. It should surface the conflict, apply temporal or source-priority filters, and, when evidence is insufficient, answer with an explicit limitation. This exposes graph errors before they become convincing hallucinations.

The main risk is incorrect entity resolution merging different objects, while missing temporal qualifiers turn a historical relationship into an apparently current one. A longer system prompt does not fix this; deterministic constraints, negative tests, and separation of untrusted data from governing instructions are required. Any automatic fallback must be visible in the trace and must not broaden permissions or sources.

Release tests should cover duplicate entities, alias collisions, orphan nodes, conflicting relationships, and expired facts. After each defect, keep a minimal graph fixture as a regression case. Rollback must restore the ontology version, graph snapshot, and retrieval policy together rather than only application code.

Core signals include entity-linking precision, relation-traversal accuracy, evidence coverage, unanswered rate for unknown entities, and answer quality against a text-only baseline. Offline evaluation should use real and adversarial queries, frozen labels, and segmentation by domain, language, and risk. An average must not hide failure in a critical segment; that segment needs its own guardrail threshold.

In production, keep stable IDs, a schema registry, constraints, a review queue for new relationships, effective dates, source pointers, and controlled ontology migration. Roll out a new configuration in shadow mode or a small canary, compare it with the baseline, and roll back automatically when the SLO is breached. This connects quality, cost, and reliability instead of optimizing an isolated retrieval metric.

A knowledge graph is useful for multi-hop relationships, but every node and edge should carry source, validFrom, validUntil, and confidence. Automatically extracted relations do not become canonical facts without validation. The query pipeline first resolves entities, then allowed relation paths, and only then gathers evidence for generation. Traversal that is too broad creates noise, latency, and cross-tenant leakage risk.

Evaluation should test entity linking, path correctness, temporal validity, and answer support. When sources conflict, the graph keeps both versions and their provenance instead of overwriting a fact. Graph updates must be idempotent and support deletion propagation. The final answer exposes the source chain so users can verify how the claims are connected.

• Do not create edges without provenance.
• Limit traversal depth.
• Test temporal conflicts.