Taxonomy vs Ontology vs Knowledge Graph: What’s the Difference for Enterprise AI?

Taxonomies, ontologies, and knowledge graphs provide complementary layers of structure for enterprise AI: taxonomies classify concepts in hierarchies for consistent routing and retrieval, ontologies define concepts, permissible relationships, and semantic rules, and knowledge graphs store current facts about specific connected entities such as suppliers, products, facilities, and events. While taxonomies answer where something belongs and ontologies clarify what it means and how it may relate, knowledge graphs answer what is connected in the business now, enabling analysis of dependencies and impacts that isolated documents or similarity search cannot provide. Used together, these layers support GraphRAG and agentic AI by combining classification, shared meaning, vector-based discovery of unstructured content, and relationship-aware retrieval of enterprise context. Organizations may need only a taxonomy for content organization, an ontology when cross-system definitions require standardization, or a knowledge graph when decisions depend on real-world relationships in areas such as supply chains, fraud, cybersecurity, and customer intelligence; TigerGraph is presented as a platform for modeling these relationships and combining graph and vector capabilities.

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