A network of entities and their semantic relationships, enabling machines to understand context.
A knowledge graph is a structured representation of real-world entities and the relationships between them, stored as a network of nodes (entities) and edges (relationships). Unlike relational databases that store data in tables, knowledge graphs store data as triples — subject-predicate-object statements like 'Apple-manufactures-iPhone' or 'Einstein-bornIn-Germany.' This structure allows machines to traverse relationships, understand context, and reason about complex domains.
Knowledge graphs have become the connective tissue of enterprise AI. As AI systems need to understand not just what data says but what it means in context, knowledge graphs provide the semantic backbone. Major technology companies — Google, Microsoft, Amazon — have invested billions in knowledge graph infrastructure. In 2026, enterprise knowledge graphs are increasingly used to ground AI agents, prevent hallucinations, and provide verifiable, traceable reasoning.
Knowledge graphs are built by extracting entities and relationships from structured and unstructured data sources, then linking them through a common ontology. They are stored in specialized graph databases (triple stores) and queried using SPARQL. Modern knowledge graphs often incorporate vector embeddings alongside symbolic representations, creating 'neurosymbolic' systems that combine the pattern-recognition power of neural networks with the logical precision of symbolic AI.
Google's Knowledge Graph powers the information panels you see when searching for a person, place, or organization. When you search 'Elon Musk,' Google's knowledge graph instantly surfaces his companies, relationships, and key facts — not by searching documents, but by traversing a pre-built network of semantic relationships.