Context graphs vs knowledge graphs

A knowledge graph connects entities through meaningful relationships. Think of suppliers, products, contracts and the dependencies between them. It gives AI a structured model of a business domain.

A context graph emphasizes the circumstances surrounding a task or decision. It may connect goals, events, permissions, evidence and earlier decisions. The term is evolving; this is a useful distinction, not a rigid boundary.

The two approaches complement each other. A knowledge graph can also contain history and context. A context graph brings those connections into focus for a particular situation.

In Neo4j, both can be represented using nodes, relationships and properties. GraphRAG can combine semantic retrieval with graph traversal to assemble relevant evidence. An agent can use that evidence to choose a better next step.

Consider a delayed order: dependencies reveal affected customers, while context identifies applicable commitments. Together, these connections support more relevant answers and explainable actions. Their value still depends on current data, provenance and enforced access controls.