Dynamic Provenance for SPARQL Update
Harry Halpin, James Cheney

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsScientific Computing and Data Management · Research Data Management Practices · Semantic Web and Ontologies
