While the (Semantic) Web currently does have a way to exhibit static provenance information in the W3C PROV standards, the Web does not have a way to describe dynamic changes to data. While some provenance models and annotation techniques originally developed with databases or workflows in mind transfer readily to RDF, RDFS 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. [1] to RDF datasets that change over time in response to SPARQL updates, how to represent the resulting provenance records themselves as RDF using named graphs in a manner compatible with W3C PROV, and how the provenance information can be provided as a SPARQL query. The primary contribution 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. SPARQL Update, provenance, versioning, RDF, semantics Our hypothesis is that a simple vocabulary, composed of insert, delete, and copy operations as introduced by Buneman et al. [1], along with explicit identifiers for update steps, versioning relationships, and metadata about updates provides a flexible format for dynamic provenance on the Semantic Web. A primary advantage of our methodology is it keeps the changes to raw data separate from the changes in provenance metadata, so legacy applications will continue to work and the cost of storing and providing access to provenance can be isolated from that of the raw data.
Halpin and Cheney REF consider a provenance model for SPARQL queries and updates to data stores involving named graphs, whose purpose is to provide a record of how the raw data in a dataset has changed over time.
Dynamic provenance for SPARQL updates using named graphs
WWW '14 Companion
Computer Science