Asma, Z., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024). NPCS: Native Provenance Computation for SPARQL. In WWW ’24: Proceedings of the ACM Web Conference 2024 (pp. 2085–2093). ACM. https://doi.org/10.1145/3589334.3645557
data provenance; how-provenance; knowledge graphs; rdf; sparql
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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.
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Project title:
Aktivierung von auf Röntgen-CT basierenden Prozessketten der Industrie 4.0 durch die Ausbildung von Forschungsexperten der nächsten Generation: MSCA ITN-2020-ETN-956172 (European Commission) Scalable Reasoning in Knowledge Graphs: VRG18-013 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds)
CC BY 4.0
