RDFgraafidel

RDFgraafidel is a hypothetical metadata framework proposed to accompany RDF datasets by representing a unit called a graafidel, which captures the fidelity, provenance, and integrity of a graph or subgraph. The concept aims to provide verifiable evidence about how a graph was produced, transformed, and versioned, supporting reproducibility and trust in data workflows and knowledge graphs.

In this concept, a graafidel is described as an RDF resource identified by a URI and carrying properties that describe version, a graph-level hash, scope, and provenance. It is designed to work alongside established vocabularies such as PROV-O for provenance and DQV for data quality, by introducing a compact, graph-centric extension vocabulary under a dedicated namespace. Semantics are defined to ensure that a graafidel applies to a specific graph or subgraph and to record derivation steps, timestamps, and responsible agents. Interoperability is achieved by reusing standard RDF constructs and linking graafidels to the datasets they describe.

Potential use cases include dataset verification, change tracking, audit trails, and reproducible research. Limitations include lack of formal standardization, potential overhead for data providers, and the need for broad adoption of the accompanying vocabulary. The concept remains theoretical and is presented here as an example of how graph fidelity metadata could be modeled within RDF.

Example (Turtle):

<http://example.org/rdfg/graafidel/1> a <http://example.org/rdfg#Graafidel> ;

<http://example.org/rdfg#version> "1.0" ;

<http://example.org/rdfg#graphHash> "e3b0c44298fc1c149afbf4c8996fb924" ;

<http://www.w3.org/ns/prov#startedAtTime> "2024-01-01T12:00:00Z"^^<http://www.w3.org/2001/XMLSchema#dateTime> ;

<http://www.w3.org/ns/prov#wasDerivedFrom> <http://example.org/rdfg/dataset/v1> .

RDFgraafidel is not an official standard as of this writing; it is a conceptual proposal used here to illustrate how graph fidelity metadata could be modeled within RDF. See also RDF, PROV-O, OWL, and data provenance.