Research brief · 2026-07-13

Enterprise knowledge graphs: construction, maintenance, and use with LLMs

A knowledge graph turns an organisation's documents, people and processes into typed entities and relationships, so an AI system can reason across them instead of guessing from adjacent text.

TL;DR. Knowledge graphs turn scattered enterprise data into linked entities a model can reason across. They earn their keep in relationship-heavy domains: org structure, product hierarchies, regulation. AIKm builds graphs incrementally: extract entities from existing documents, link them to authoritative registries, and expose a subgraph to the retrieval layer. Combined with RAG, the graph gives agents a citable structure to reason about, not just passages to quote, which raises answer precision on multi-hop questions.

Why a graph, and when

Text retrieval alone struggles when the answer is a relationship: which policies apply to a joint venture; which supplier owns this defect; who approves this contract in this region. A knowledge graph makes those relationships first-class, and lets an LLM traverse them with citations.

Layers of an enterprise graph

Layer	Contents	Owner
Ontology	Entity and relationship types, controlled vocabularies	Domain experts + data team
Instance data	People, products, contracts, incidents, sites, controls	Source systems + extractors
Provenance	Source document, extraction confidence, timestamp	Pipeline
Access	Row and edge level permissions	Security + IT

Construction pipeline

Extract entities with a mix of rules, dictionaries and LLM-based extractors; resolve duplicates against a canonical registry; validate against the ontology; write to the graph with a provenance record. Every edge should be attributable to at least one source document.

Keeping the graph fresh

Batch rebuilds miss the point. AIKm's default is event-driven: when a source system updates, the pipeline re-extracts only the affected subgraph and diffs it against the current state. Human review is triggered when confidence is low or a policy-tagged edge changes.

Querying with an LLM

Combine a graph query (Cypher, SPARQL, or a typed API) with retrieval over the source passages behind each edge. The model answers using both, and cites the underlying documents. See the graph-augmented RAG pattern.

Governance considerations

A graph makes sensitive relationships more discoverable, not less. Access control belongs at the edge level, and audit logs should record which subgraph was returned for each answer. This aligns with the agentic AI governance checklist.

Discuss a knowledge graph project