Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata

Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this...

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Detalles Bibliográficos
Autores principales: Turki, Houcemeddine, Jemielniak, Dariusz, Hadj Taieb, Mohamed A., Labra Gayo, Jose E., Ben Aouicha, Mohamed, Banat, Mus’ab, Shafee, Thomas, Prud’hommeaux, Eric, Lubiana, Tiago, Das, Diptanshu, Mietchen, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Bioinformatics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575845/
https://www.ncbi.nlm.nih.gov/pubmed/36262159
http://dx.doi.org/10.7717/peerj-cs.1085
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author Turki, Houcemeddine
Jemielniak, Dariusz
Hadj Taieb, Mohamed A.
Labra Gayo, Jose E.
Ben Aouicha, Mohamed
Banat, Mus’ab
Shafee, Thomas
Prud’hommeaux, Eric
Lubiana, Tiago
Das, Diptanshu
Mietchen, Daniel
author_facet Turki, Houcemeddine
Jemielniak, Dariusz
Hadj Taieb, Mohamed A.
Labra Gayo, Jose E.
Ben Aouicha, Mohamed
Banat, Mus’ab
Shafee, Thomas
Prud’hommeaux, Eric
Lubiana, Tiago
Das, Diptanshu
Mietchen, Daniel
author_sort Turki, Houcemeddine
collection PubMed
description Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.
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spelling pubmed-95758452022-10-18 Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata Turki, Houcemeddine Jemielniak, Dariusz Hadj Taieb, Mohamed A. Labra Gayo, Jose E. Ben Aouicha, Mohamed Banat, Mus’ab Shafee, Thomas Prud’hommeaux, Eric Lubiana, Tiago Das, Diptanshu Mietchen, Daniel PeerJ Comput Sci Bioinformatics Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research. PeerJ Inc. 2022-09-29 /pmc/articles/PMC9575845/ /pubmed/36262159 http://dx.doi.org/10.7717/peerj-cs.1085 Text en © 2022 Turki et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Turki, Houcemeddine
Jemielniak, Dariusz
Hadj Taieb, Mohamed A.
Labra Gayo, Jose E.
Ben Aouicha, Mohamed
Banat, Mus’ab
Shafee, Thomas
Prud’hommeaux, Eric
Lubiana, Tiago
Das, Diptanshu
Mietchen, Daniel
Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata
title Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata
title_full Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata
title_fullStr Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata
title_full_unstemmed Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata
title_short Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata
title_sort using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of covid-19 epidemiology in wikidata
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575845/
https://www.ncbi.nlm.nih.gov/pubmed/36262159
http://dx.doi.org/10.7717/peerj-cs.1085
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