Taxonomy vs. ontology vs. knowledge graph: What’s the difference?

The blog post by John Stegeman, a Graph Database Product Specialist at Neo4j, explores the differences and interconnections between taxonomy, ontology, and knowledge graphs, emphasizing their roles in transforming raw data into actionable knowledge for AI systems. Taxonomies provide hierarchical categorization, ontologies define semantic meanings and logical connections, and knowledge graphs integrate these elements to organize and interpret data using nodes, relationships, and properties. Knowledge graphs, supported by Neo4j's platform, enable more intelligent reasoning in AI by allowing systems to retrieve and process connected data contextually, enhancing the accuracy and explainability of AI outputs. The post also highlights how Neo4j's tools, such as neosemantics and GraphRAG, facilitate the creation and management of knowledge graphs, aiding in the development of AI systems that can efficiently navigate and reason with complex data structures.

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