RDFbased

RDFbased is a term used to describe systems, methods, and data models that are built around the Resource Description Framework (RDF) as the foundational representation of information. In RDF, knowledge is expressed as triples—subject, predicate, and object—forming a graph that can integrate data from diverse sources through shared identifiers.

RDFbased data employs URIs for subjects and predicates and supports literals and blank nodes. Data can be serialized in formats such as RDF/XML, Turtle, N-Triples, and JSON-LD, enabling interchange while preserving graph structure. Core standards include RDF itself, RDF Schema (RDFS), and the Web Ontology Language (OWL), which provide vocabularies and logical semantics to support reasoning and interoperability.

Querying RDFbased data is typically done with SPARQL, a graph query language that can extract patterns, perform joins, and support inferencing when combined with ontologies. Practitioners often use RDFbased approaches for constructing knowledge graphs, metadata catalogs, linked open data, and integrated datasets in domains like science, cultural heritage, and enterprise data management.

An RDFbased architecture usually comprises a triple store or graph database, data ingestion pipelines, ontology repositories, and reasoners. It enables flexible schema evolution, schema mediation, and semantic interoperability across heterogeneous datasets. Benefits include explicit semantics, improved data integration, and the capacity to publish and link data on the web.

Challenges involve scalability, performance at large scale, data quality, governance, and the learning curve associated with RDF and SPARQL. Despite these, RDFbased approaches remain foundational to the semantic web and modern knowledge graphs.

See also: RDF, SPARQL, Linked Data, RDF Schema, OWL.