Graphs, knowledge graphs, & context graphs: Which one do you need?

Graphs model data as interconnected nodes and relationships, helping organizations identify patterns in areas such as fraud, recommendations, supply chains, cybersecurity, and customer analysis that may be difficult to see in traditional tables. Knowledge graphs extend this model by adding shared definitions, taxonomies, ontologies, and business rules, creating a governed view that clarifies what entities and relationships mean and supports discovery, compliance, and AI grounding. Context graphs add enterprise knowledge, conversational history, and prior decision traces to provide the most relevant information for a specific task or moment, particularly for AI agents that require accurate, explainable decisions and persistent memory. GraphRAG is presented as a retrieval method that uses graph relationships to supply connected context to generative AI rather than isolated documents, while knowledge and context graphs together can form a knowledge layer linking enterprise data, models, workflows, and actions.

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