Knowledge Graph

A knowledge graph is a data model that represents real-world things as nodes (entities) and the relationships between them as labelled edges, often as subject-predicate-object triples. It combines the flexibility of a graph with a shared vocabulary that gives the links meaning.

Why it matters

Knowledge graphs make connections first-class. They support questions such as "which suppliers provide parts used by products in this region" without complex joins, and they help search engines, recommendation systems and AI assistants ground answers in structured facts. They are also used to disambiguate entities, for example telling a company from a similarly named person. Building and maintaining one takes real effort in schema design and data quality.

Example

(NJHPS)     -[located_in]->  (Jhakri)
(Jhakri)    -[part_of]->     (Himachal Pradesh)
(Himachal Pradesh) -[country]-> (India)

Query: all power stations in Himachal Pradesh

Common mistakes

• Starting without a clear schema, which yields a tangle of inconsistent labels.
• Treating any graph database as a knowledge graph. The semantics are what matter.
• Neglecting updates, so facts go stale.

Related

• Graph database
• Structured vs unstructured data
• Semantic search
• RAG

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