A knowledge graph turns data into a web of meaning. Each node is an entity; each edge is a typed relationship. Because the connections are explicit, you can ask questions that span many hops: which outcomes a capability supports, which processes a decision touches.
Knowledge graphs pair naturally with an ontology. The ontology defines the allowed entity types and relationship types; the graph is the populated instance. Together they make context machine-readable, which is what lets AI answers stay grounded and traceable. An LLM left to guess produces plausible text; reasoning over a governed graph, it produces answers you can follow to source.