Information stored as connections
A knowledge graph stores information about people, places, companies, products, and events together with the relationships between them.
Connect person A works at company B with company B developed product C, and a path exists between person A and product C. Rather than listing information in tables, it represents it as a network of nodes and edges.
How AI uses it
- Distinguishing which person or product a search term refers to
- Exploring relationships between people, departments, documents, and projects
- Recommending based on connections between products or works
- Investigating chains in fraud or dependency analysis
- Supplying relationship-aware material to a model in RAG
In AI search it distinguishes different entities sharing a name and follows several relationships from one question. In Graph RAG, retrieval can follow connections between people, organizations, and events rather than only text similarity.
How it differs from a vector database
A vector database converts text or images into numbers and excels at finding things with similar meaning. A knowledge graph excels at following explicitly stated relationships — who made what, which component goes into which product.
They are complementary rather than competing. Finding relevant text through vector search and confirming relationships through a graph gives a model more precise context.
Cautions
A knowledge graph assumes the underlying information is correctly organized. Register stale information or a wrong relationship and the model will answer from it.
Who defines the relationships, and how updates continue, matters as much. A path existing in the graph does not prove causation or fact. For consequential decisions, check the underlying data and its source.