Knowledge Graph

Technical explanation

Knowledge graphs commonly model facts as nodes and typed edges or subject–predicate–object statements. Identifiers, schemas or ontologies, provenance and entity resolution help combine data; graph query languages and reasoning can retrieve or infer connected information.

Business relevance

They connect fragmented data around shared entities, supporting search, recommendations, analytics, integration, traceability and context for AI systems.

Implementation example

A company graph links customers, contracts, products, support cases and owners through stable identifiers so authorised teams can query relationships across source systems.

Limitations and common misconceptions

A graph is not automatically true or complete. Entity matching errors, stale relationships, weak provenance, access-control failures and poorly governed ontologies can propagate misleading results.

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