Knowledge Graph Querying

Version: 1.0.0 | Last updated: 2026-07-16 | Maturity: Emerging for LLM-generated queries

Purpose

Use graph traversal safely for entity, relationship, temporal, and multi-hop retrieval.

Why

Graph retrieval can answer queries poorly served by lexical or vector search, but unconstrained generated queries can leak data or produce expensive traversals.

How

Map each validated query class to parameterized templates or a constrained query AST. Resolve entities within tenant scope, apply node/edge ACL and temporal predicates before traversal, enforce path depth/cardinality/cost limits, and return source-backed subgraphs. If an LLM proposes a query, parse, authorize, estimate, and execute it through a read-only gateway. Evaluate against vector/keyword baselines.

Tradeoffs

Templates are safer and predictable but less flexible. Generated graph queries remain emerging and require a sandbox and deterministic policy.

Anti-patterns

Executing raw model-generated Cypher/SPARQL.
Applying ACL only to final nodes.
Presenting a path as causal evidence without source support.

Enterprise Considerations

Use read replicas, workload limits, tenant partitions, query audit logs, and denial-of-wallet alerts.

Checklist

Traversal is ACL- and time-filtered at every hop.
Queries are parameterized, bounded, and read-only.
Returned edges resolve to authorized evidence.
Graph retrieval demonstrates benefit per query class.

References

Changelog

1.0.0 — 2026-07-16: Initial emerging-practice standard.