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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
Authors: 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
Contributors: Ministry of Higher Education and Scientific Research in Tunisia; Wikimedia Foundation; WikiCred Grants Initiative of Craig Newmark Philanthropies, Facebook, and Microsoft; Spanish Ministry of Economy and Competitiveness; Alfred P. Sloan Foundation; Polish National Science Center
Source: PeerJ Computer Science ; volume 8, page e1085 ; ISSN 2376-5992
Publisher Information: PeerJ
Publication Year: 2022
Collection: PeerJ (E-Journal - via CrossRef)
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.
Document Type: article in journal/newspaper
Language: English
DOI: 10.7717/peerj-cs.1085
Availability: https://doi.org/10.7717/peerj-cs.1085; https://peerj.com/articles/cs-1085.pdf; https://peerj.com/articles/cs-1085.xml; https://peerj.com/articles/cs-1085.html
Rights: https://creativecommons.org/licenses/by/4.0/
Accession Number: edsbas.F92096B4
Database: BASE