%0 Conference Paper %1 DBLP:conf/www/Galarraga0KH23 %A Galárraga, Luis %A Hernández, Daniel %A Katim, Anas %A Hose, Katja %B Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 %D 2023 %E Ding, Ying %E Tang, Jie %E Sequeda, Juan F. %E Aroyo, Lora %E Castillo, Carlos %E Houben, Geert-Jan %I ACM %K ac-coffee myown ac-krbuilding peer from:danielhz %P 212-216 %R 10.1145/3543873.3587350 %T Visualizing How-Provenance Explanations for SPARQL Queries %U https://doi.org/10.1145/3543873.3587350 %X 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.

@inproceedings{DBLP:conf/www/Galarraga0KH23, 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. }, added-at = {2023-05-31T13:10:38.000+0200}, author = {Galárraga, Luis and Hernández, Daniel and Katim, Anas and Hose, Katja}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/27a63ca97f5c8ed53f5f75ba297605894/analyticcomp}, booktitle = {Companion Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023}, doi = {10.1145/3543873.3587350}, editor = {Ding, Ying and Tang, Jie and Sequeda, Juan F. and Aroyo, Lora and Castillo, Carlos and Houben, Geert-Jan}, eventdate = {30 April 2023 - 4 May 2023}, eventtitle = {ACM Web Conference 2023, WWW 2023}, interhash = {a3d835c267e244d8deb217f7836262dd}, intrahash = {7a63ca97f5c8ed53f5f75ba297605894}, keywords = {ac-coffee myown ac-krbuilding peer from:danielhz}, language = {English}, pages = {212-216}, publisher = {ACM}, timestamp = {2023-10-11T14:13:39.000+0200}, title = {Visualizing How-Provenance Explanations for SPARQL Queries}, url = {https://doi.org/10.1145/3543873.3587350}, venue = {Austin, Texas, USA}, year = 2023 }