What is a linked data model in knowledge graphs?

A linked data model in knowledge graphs is a structured approach to connecting and representing data using standardized web technologies. It relies on principles like unique identifiers (URIs), semantic relationships, and open formats to create a network of interconnected information. Unlike traditional databases, which store data in isolated tables, linked data models emphasize linking entities across datasets, enabling machines and humans to navigate and query relationships efficiently. For example, a knowledge graph about cities might use URIs like http://example.org/Paris to represent Paris, with links to other entities (e.g., capitalOf France) using RDF (Resource Description Framework) triples.

In practice, linked data models use RDF to structure data as subject-predicate-object triples. For instance, the statement “Paris is the capital of France” becomes <Paris> <capitalOf> <France>, where each element is a URI or literal. This format allows data to be merged from multiple sources, as long as they adhere to shared vocabularies or ontologies like Schema.org or OWL (Web Ontology Language). Developers can query these graphs using SPARQL, a language designed for traversing linked data. For example, a query might retrieve all cities that are capitals of countries in Europe, even if the data is stored across different knowledge bases like DBpedia or Wikidata.

The strength of linked data models lies in their interoperability and scalability. By using URIs, datasets can reference the same entities unambiguously, reducing duplication. For instance, a medical knowledge graph might link patient data to drug databases using shared identifiers, enabling cross-domain analysis. Tools like Apache Jena or RDFLib help developers parse, store, and query RDF data, while frameworks like GraphDB provide graph-specific storage optimized for linked data. This approach is particularly useful for applications requiring integration of diverse data sources, such as recommendation systems or semantic search engines, where understanding relationships between entities is critical.