Knowledge Graph
A knowledge graph stores information as entities and the relationships between them, rather than as loose documents or vectors. For AI, it gives a model structured, connected context it can traverse — which is one answer to grounding and hallucination.
Also known as: knowledge graphs
Most retrieval today is vector-based: chunk documents, embed them, fetch the nearest. A knowledge graph takes a different shape — it represents data as nodes (entities like people, products, accounts) and edges (the relationships between them). That structure lets a system answer questions that depend on connections, not just similarity, and traverse from one fact to related ones.
For grounding AI, graphs and vectors are complementary. Vector retrieval is great at “find me text that’s about this”; a graph is great at “what’s connected to this, and how.” Pairing them — sometimes called graph RAG — is a growing approach to reducing hallucination by giving the model trustworthy, structured context instead of a pile of loosely related chunks.
