Visualizing How-Provenance Explanations for SPARQL Queries

Luis Galárraga; Daniel Hernández; Anas Katim; Katja Hose

doi:10.1145/3543873.3587350

Visualizing How-Provenance Explanations for SPARQL Queries

Luis Galárraga, Daniel Hernández, Anas Katim, Katja Hose

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

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.

Original language	English
Title of host publication	ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
Number of pages	5
Publisher	Association for Computing Machinery
Publication date	30 Apr 2023
Pages	212-216
ISBN (Electronic)	978-1-4503-9419-2
DOIs	https://doi.org/10.1145/3543873.3587350
Publication status	Published - 30 Apr 2023
Event	The ACM Web Conference 2023 - Austin, United States Duration: 30 Apr 2023 → 4 May 2023

Conference

Conference	The ACM Web Conference 2023
Country/Territory	United States
City	Austin
Period	30/04/2023 → 04/05/2023

Keywords

RDF
SPARQL
how-provenance
query provenance

Access to Document

10.1145/3543873.3587350

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Cite this

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title = "Visualizing How-Provenance Explanations for SPARQL Queries",

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.",

keywords = "RDF, SPARQL, how-provenance, query provenance",

author = "Luis Gal{\'a}rraga and Daniel Hern{\'a}ndez and Anas Katim and Katja Hose",

year = "2023",

month = apr,

day = "30",

doi = "10.1145/3543873.3587350",

language = "English",

pages = "212--216",

booktitle = "ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023",

publisher = "Association for Computing Machinery",

address = "United States",

note = "The ACM Web Conference 2023 ; Conference date: 30-04-2023 Through 04-05-2023",

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Visualizing How-Provenance Explanations for SPARQL Queries. / Galárraga, Luis; Hernández, Daniel; Katim, Anas et al.
ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023. Association for Computing Machinery, 2023. p. 212-216.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Visualizing How-Provenance Explanations for SPARQL Queries

AU - Galárraga, Luis

AU - Hernández, Daniel

AU - Katim, Anas

AU - Hose, Katja

PY - 2023/4/30

Y1 - 2023/4/30

N2 - 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.

AB - 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.

KW - RDF

KW - SPARQL

KW - how-provenance

KW - query provenance

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U2 - 10.1145/3543873.3587350

DO - 10.1145/3543873.3587350

M3 - Article in proceeding

SP - 212

EP - 216

BT - ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023

PB - Association for Computing Machinery

T2 - The ACM Web Conference 2023

Y2 - 30 April 2023 through 4 May 2023

ER -

Visualizing How-Provenance Explanations for SPARQL Queries

Abstract

Conference

Keywords

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AUB Link

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Poul Due Jensen Professorate in Big Data and Artificial Intelligence

RelWeb: A Reliable Web of Data

Cite this

Visualizing How-Provenance Explanations for SPARQL Queries

Abstract

Conference

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Projects

Poul Due Jensen Professorate in Big Data and Artificial Intelligence

RelWeb: A Reliable Web of Data

Cite this