A knowledge graph is a network of entities—people, products, places, concepts—connected by labeled relationships. Instead of storing flat text, it stores facts as structured links, letting systems query connections directly and answer questions that require reasoning across relationships.
Why it matters for search and AI
Traditional search matches keywords; a knowledge graph understands that 'Paris' is a city, that it's the capital of France, and that it relates to other entities like landmarks or people. This lets systems answer multi-hop questions and power features like entity recognition in search results.
How it's built
Knowledge graphs are constructed by extracting entities and relationships from structured databases, documents, or APIs, then linking them under a shared schema. Companies like Google and Amazon use them internally to connect product data, customer behavior, and content metadata.
Where it shows up in commerce
E-commerce platforms use knowledge graphs to link products, categories, brands, and customer intent, improving recommendations and search relevance beyond simple keyword matching. This is a foundational layer for retrieval-augmented generation (RAG) systems that need grounded, connected facts.
Knowledge graphs vs. vector search
Vector search finds semantically similar content; knowledge graphs encode explicit, verified relationships between facts. Many modern AI systems combine both—vectors for fuzzy matching, graphs for precise reasoning—to reduce hallucination and improve answer accuracy.
Frequently asked
Is a knowledge graph the same as a database?
No. A database stores records in tables, while a knowledge graph explicitly models relationships between entities as first-class data.
Do small companies need knowledge graphs?
Not always—they add value when you have complex, interconnected data like product catalogs, customer relationships, or content taxonomies that benefit from explicit linking.
How do knowledge graphs help reduce AI hallucination?
By grounding AI responses in verified, structured facts and relationships instead of relying solely on generated text patterns.