What Is a Knowledge Graph? Structuring Facts as Connected Relationships
Instead of storing facts as isolated text, a knowledge graph represents them as explicit relationships between entities — a structure some AI systems use for more precise retrieval.
Facts as connected relationships
A knowledge graph represents information as entities (people, places, concepts) connected by explicit, labeled relationships — "Company X acquired Company Y in 2023" becomes a structured connection rather than just a sentence buried in unstructured text, letting relationships between facts be queried directly.
Why this differs from a vector database
A vector database finds content that's semantically similar to a query; a knowledge graph can answer precise relational questions — "which companies did Company X acquire" — that require following explicit connections rather than just finding conceptually related text.
Combining both in retrieval
Some advanced RAG systems use both together: a knowledge graph for precise, structured relational questions, and vector-based semantic search for the broader, less structured retrieval a graph alone can't cover — each covering the other's blind spot.
Frequently Asked Questions
What is a knowledge graph?
A way of representing information as entities connected by explicit, labeled relationships, allowing precise questions about how facts relate to each other, rather than storing facts as isolated, unstructured text.
How is a knowledge graph different from a vector database?
A vector database finds content that's semantically similar to a query; a knowledge graph can answer precise relational questions by following explicit connections between entities, which pure semantic similarity search can't do.