<div class="csl-bib-body">
<div class="csl-entry">Galárraga, L., Hernández, D., Katim, A., & Hose, K. (2023). Visualizing How-Provenance Explanations for SPARQL Queries. In <i>WWW ’23 Companion: Companion Proceedings of the ACM Web Conference 2023</i> (pp. 212–216). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587350</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/177099
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dc.description.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.
en
dc.language.iso
en
-
dc.subject
SPARQL
en
dc.subject
RDF
en
dc.subject
how-provenance
en
dc.subject
query provenance
en
dc.title
Visualizing How-Provenance Explanations for SPARQL Queries
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
Inria Centre de Recherche Rennes Bretagne Atlantique
-
dc.contributor.affiliation
University of Stuttgart, Germany
-
dc.contributor.affiliation
INSA Rouen
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dc.relation.isbn
9781450394192
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dc.description.startpage
212
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dc.description.endpage
216
-
dc.type.category
Full-Paper Contribution
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tuw.booktitle
WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023
-
tuw.peerreviewed
true
-
tuw.relation.publisher
Association for Computing Machinery
-
tuw.relation.publisherplace
New York
-
tuw.researchTopic.id
I1
-
tuw.researchTopic.id
I4a
-
tuw.researchTopic.name
Logic and Computation
-
tuw.researchTopic.name
Information Systems Engineering
-
tuw.researchTopic.value
80
-
tuw.researchTopic.value
20
-
tuw.publication.orgunit
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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tuw.publication.orgunit
E192 - Institut für Logic and Computation
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tuw.publisher.doi
10.1145/3543873.3587350
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dc.description.numberOfPages
5
-
tuw.author.orcid
0000-0002-0241-5379
-
tuw.author.orcid
0000-0002-7896-0875
-
tuw.author.orcid
0009-0009-0406-9527
-
tuw.author.orcid
0000-0001-7025-8099
-
tuw.event.name
ACM Web Conference 2023
en
dc.description.sponsorshipexternal
Independent Research Fund Denmark
-
dc.description.sponsorshipexternal
DFG
-
dc.description.sponsorshipexternal
EU Horizon 2020
-
dc.relation.grantnoexternal
8048-00051B
-
dc.relation.grantnoexternal
STA 572_15-2
-
dc.relation.grantnoexternal
952215
-
tuw.event.startdate
30-04-2023
-
tuw.event.enddate
05-05-2023
-
tuw.event.online
On Site
-
tuw.event.type
Event for scientific audience
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tuw.event.place
Austin, Texas
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tuw.event.country
US
-
tuw.event.presenter
Luis Galárraga
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tuw.event.presenter
Daniel Hernández
-
tuw.event.presenter
Katja Hose
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wb.sciencebranch
Informatik
-
wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
-
wb.sciencebranch.oefos
1010
-
wb.sciencebranch.value
80
-
wb.sciencebranch.value
20
-
item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.fulltext
no Fulltext
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item.cerifentitytype
Publications
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item.grantfulltext
none
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item.openairetype
conference paper
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item.languageiso639-1
en
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crisitem.author.dept
Inria Centre de Recherche Rennes Bretagne Atlantique
-
crisitem.author.dept
University of Stuttgart
-
crisitem.author.dept
INSA Rouen
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence