Provenance Management for Evolving RDF Datasets.

Tracking the provenance of information published on the Web is of crucial importance for effectively supporting trustworthiness, accountability and repeatability in the Web of Data. Although extensive work has been done on computing the provenance for SPARQL queries, little research has been conducted for the case of SPARQL updates. This paper proposes a new provenance model that borrows properties from both how and where provenance models, and is suitable for capturing the triple and attribute level provenance of data introduced via SPARQL INSERT updates. To the best of our knowledge, this is the first model that deals with the provenance of SPARQL updates using algebraic expressions, in the spirit of the well-established model of provenance semirings. We present an algorithm that records the provenance of SPARQL update results, and a reconstruction algorithm that uses this provenance to identify a SPARQL update that is compatible to the original one, given only the recorded provenance. Our approach is implemented and evaluated on top of Virtuoso Database Engine.