Knowledge Graph

A Knowledge Graph is a structured representation of entities (people, products, concepts) and the relationships between them, used to enrich AI reasoning and retrieval. Famous knowledge graphs include Google's Knowledge Graph (powering search results and answer boxes), Wikidata, DBpedia, and the proprietary graphs maintained by Amazon, LinkedIn, and Meta. In enterprise AI, knowledge graphs capture entities like customers, products, employees, contracts, suppliers, and the relationships between them. Combined with LLMs, knowledge graphs enable Graph-RAG — retrieval augmented by structured relationships rather than just vector similarity. Tools include Neo4j, TigerGraph, Amazon Neptune, and the increasingly popular GraphRAG framework from Microsoft Research. Knowledge graphs add reasoning capabilities that pure vector search cannot match — for example, multi-hop questions like "who reports to my manager's manager?" AI governance frameworks treat knowledge graphs as sensitive data assets requiring access controls, AI compliance review, and AI risk management oversight as part of any responsible AI program.

Centralpoint Pairs Knowledge Graphs With Modern AI: Oxcyon's platform integrates knowledge-graph reasoning with model-agnostic LLM access — ChatGPT, Gemini, Llama, embedded. Centralpoint meters every LLM call, keeps prompts and skills on-premise, and embeds graph-aware chatbots into your portals with a single line of JavaScript.


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