Dynamic Provenance for SPARQL Update

TL;DR

This paper develops a semantic framework linking SPARQL Update semantics with a 'cut-and-paste' provenance model for dynamic RDF datasets, enabling provenance tracking over time.

Contribution

It introduces a novel semantic framework that adapts the Buneman et al. provenance model to RDF and SPARQL Update, compatible with W3C PROV standards.

Findings

Enables provenance tracking for dynamic RDF datasets.

Represents provenance records as RDF compatible with W3C PROV.

Provides a formal semantics linking SPARQL Update to provenance models.

Abstract

While the Semantic Web currently can exhibit provenance information by using the W3C PROV standards, there is a "missing link" in connecting PROV to storing and querying for dynamic changes to RDF graphs using SPARQL. Solving this problem would be required for such clear use-cases as the creation of version control systems for RDF. While some provenance models and annotation techniques for storing and querying provenance data originally developed with databases or workflows in mind transfer readily to RDF and SPARQL, these techniques do not readily adapt to describing changes in dynamic RDF datasets over time. In this paper we explore how to adapt the dynamic copy-paste provenance model of Buneman et al. [2] to RDF datasets that change over time in response to SPARQL updates, how to represent the resulting provenance records themselves as RDF in a manner compatible with W3C PROV, and how the provenance information can be defined by reinterpreting SPARQL updates. The primary contribution of this paper is a semantic framework that enables the semantics of SPARQL Update to be used as the basis for a 'cut-and-paste' provenance model in a principled manner.