RDFn

RDFn is a proposed extension to the Resource Description Framework (RDF) designed to enhance the representation and management of nested graphs, provenance, and temporal information. It retains the triple-based core of RDF while introducing graph-level constructs that can encapsulate subgraphs and annotations.

In RDFn, data can be grouped into named or nested graphs with explicit identifiers. This enables provenance metadata, versioning, and time-aware queries to be associated with specific subgraphs rather than only with individual triples. RDFn defines serialization options compatible with existing RDF formats, along with extensions for graph-level metadata such as creation timestamps, editors, and access controls.

Querying RDFn builds on SPARQL and adds syntax and operators for navigating nested graphs and time dimensions. Temporal filtering, subgraph scoping, and provenance-aware queries are among the core features, enabling users to reason about data as it evolves and to enforce graph-level access controls.

Development status: RDFn emerged in scholarly and industrial discussions in the early 2020s as a potential approach to address complex data integration and compliance needs. As of 2025, it remains a proposal with limited implementation, and no formal standard has been published by major standards bodies. Practitioners currently explore RDFn in research prototypes and pilot projects.

Applications commonly cited for RDFn include scientific data management, research data provenance, regulatory reporting, and knowledge graphs that require granular graph-level semantics and robust versioning.

See also: Resource Description Framework, SPARQL, named graph, provenance, time-aware databases.