RDFSn

RDFSn is a conceptual data model and query language that extends Resource Description Framework (RDF) to support streaming and versioned semantic graphs. It is designed to enable efficient ingestion, processing, and querying of continuously changing data while preserving provenance and temporal context.

RDFSn represents information as triples augmented with temporal information and provenance metadata. Each triple can be associated with a validity interval or timestamp, and triples may be grouped into named graphs with versioned histories. The model emphasizes incremental updates and windowed queries, allowing clients to retrieve data within time bounds or commit histories.

The RDFSn query facility extends SPARQL with temporal and provenance operators. It supports standard graph pattern matching while adding operators for time-window selection, historical querying, and provenance-aware filtering. Serialization formats for RDFSn are designed to be compatible with RDF serializations such as Turtle or JSON-LD, enabling gradual adoption alongside existing RDF ecosystems.

Relation to existing standards: RDFSn builds on RDF 1.1 concepts such as graphs, IRIs, literals, and blank nodes, and is designed to interoperate with RDFS and OWL reasoning layers. SPARQL 1.1 can be extended to support RDFSn queries, and many implementations aim to provide compatible endpoints with streaming extensions.

Usage and status: RDFSn is described in several draft specifications and experimental projects, with varying levels of support across toolchains. It is primarily discussed in contexts requiring real-time analytics, event streams, and IoT, where temporal and provenance dimensions are essential.