Definition: A knowledge graph is a structured representation of entities, such as people, companies, products or documents, and the relationships between them, stored so software can query and reason over connected facts.
Data is modeled as nodes (entities) and edges (relationships), often with properties on both, for example "Company A owns Subsidiary B" or "Contract C references Clause D". Graph databases or RDF stores hold the data, and query languages such as Cypher or SPARQL traverse it. A schema or ontology defines which entity and relationship types are allowed.
Vector search finds passages that are similar in meaning; a knowledge graph captures explicit structure and multi-step connections. Combining the two, sometimes called GraphRAG, can help a RAG system answer questions that depend on relationships, like ownership chains or dependencies, and makes answers easier to trace.
Typical uses include customer 360 views, fraud and risk networks, product catalogs, research libraries and master data. The hard part is usually entity resolution and keeping the graph current through a reliable data pipeline.
Ryz data engineers and NLP engineers design knowledge graphs and connect them to AI applications, working on your team.
Thanks — your message has been sent. We’ll get back to you soon.
Something went wrong while sending your message. Please try again or email info@ryzlabs.com.