A set of best practices for publishing structured data on the web so it can be interconnected and queried.
Linked Data is a set of best practices for publishing and connecting structured data on the web, developed by Tim Berners-Lee. The four principles are: use URIs as names for things; use HTTP URIs so people can look up those names; when someone looks up a URI, provide useful information using the standards (RDF, SPARQL); include links to other URIs so they can discover more things. Linked Data turns the web into a global, interconnected database.
Linked Data principles are foundational to enterprise knowledge graph construction and AI data integration. As organizations build knowledge graphs that span internal systems and external data sources, Linked Data provides the architectural principles for making those graphs navigable, interoperable, and extensible. The practice of assigning canonical URIs to business entities — products, customers, locations — is a direct application of Linked Data principles.
Linked Data is implemented by publishing RDF data at dereferenceable HTTP URIs, using standard vocabularies (Dublin Core, Schema.org, FOAF) where possible, and linking to external datasets using owl:sameAs and related properties. Tools like the Linked Data Platform (LDP) standard provide a REST API for reading and writing Linked Data resources. SPARQL endpoints allow querying across federated Linked Data sources.
The UK government publishes all its statistical data as Linked Data at data.gov.uk. Each dataset, observation, and geographic area has a unique URI. An AI system can query this data alongside DBpedia's geographic data and Wikidata's political data to answer complex questions like 'How has unemployment changed in regions that voted Leave in the 2016 Brexit referendum?' — by following links between datasets.