<div class="csl-bib-body">
<div class="csl-entry">Asma, Z., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024). NPCS: Native Provenance Computation for SPARQL. In <i>WWW ’24: Proceedings of the ACM Web Conference 2024</i> (pp. 2085–2093). ACM. https://doi.org/10.1145/3589334.3645557</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/210848
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dc.description.abstract
The popularity of Knowledge Graphs (KGs) both in industry and academia owes credit to their flexible data model, suitable for data integration from multiple sources. Several KG-based applications such as trust assessment or view maintenance on dynamic data rely on the ability to compute provenance explanations for query results. The how-provenance of a query result is an expression that encodes the records (triples or facts) that explain its inclusion in the result set. This article proposes NPCS, a Native Provenance Computation approach for SPARQL queries. NPCS annotates query results with their how-provenance. By building upon spm-provenance semirings, NPCS supports both monotonic and non-monotonic SPARQL queries. Thanks to its reliance on query rewriting techniques, the approach is directly applicable to already deployed SPARQL engines using different reification schemes - including RDF-star. Our experimental evaluation on two popular SPARQL engines (GraphDB and Stardog) shows that our novel query rewriting brings a significant runtime improvement over existing query rewriting solutions, scaling to RDF graphs with billions of triples.
en
dc.description.sponsorship
European Commission
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dc.description.sponsorship
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
data provenance
en
dc.subject
how-provenance
en
dc.subject
knowledge graphs
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dc.subject
rdf
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dc.subject
sparql
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dc.title
NPCS: Native Provenance Computation for SPARQL
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.relation.publication
WWW '24: Proceedings of the ACM Web Conference 2024
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dc.contributor.affiliation
University of Crete, Greece
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dc.contributor.affiliation
University of Stuttgart, Germany
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dc.contributor.affiliation
Inria Rennes - Bretagne Atlantique Research Centre, France
