Galárraga, L., Hernández, D., Katim, A., & Hose, K. (2023). Visualizing How-Provenance Explanations for SPARQL Queries. In WWW ’23 Companion: Companion Proceedings of the ACM Web Conference 2023 (pp. 212–216). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587350
E192-02 - Forschungsbereich Databases and Artificial Intelligence E192 - Institut für Logic and Computation
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Published in:
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
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ISBN:
9781450394192
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Date (published):
Apr-2023
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Event name:
ACM Web Conference 2023
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Event date:
30-Apr-2023 - 5-May-2023
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Event place:
Austin, Texas, United States of America (the)
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Number of Pages:
5
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Publisher:
Association for Computing Machinery, New York
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Peer reviewed:
Yes
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Keywords:
SPARQL; RDF; how-provenance; query provenance
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Abstract:
Knowledge graphs (KGs) are vast collections of machine-readable information, usually modeled in RDF and queried with SPARQL. KGs have opened the door to a plethora of applications such as Web search or smart assistants that query and process the knowledge contained in those KGs. An important, but often disregarded, aspect of querying KGs is query provenance: explanations of the data sources and transformations that made a query result possible. In this article we demonstrate, through a Web application, the capabilities of SPARQLprov, an engine-agnostic method that annotates query results with how-provenance annotations. To this end, SPARQLprov resorts to query rewriting techniques, which make it applicable to already deployed SPARQL endpoints. We describe the principles behind SPARQLprov and discuss perspectives on visualizing how-provenance explanations for SPARQL queries.
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Project (external):
Independent Research Fund Denmark DFG EU Horizon 2020
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Project ID:
8048-00051B STA 572_15-2 952215
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Research Areas:
Logic and Computation: 80% Information Systems Engineering: 20%