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

• Published in: PeerJ. Computer science Vol. 8; p. e1085
• Main 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
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ. Ltd 29.09.2022; PeerJ, Inc; PeerJ Inc
• Subjects: Bioinformatics; COVID-19; COVID-19 epidemiology; Data Science; Epidemics; Epidemiology; Germany; Graphs; Knowledge graph refinement; Knowledge representation; New York; Ontology; Public health surveillance; Query languages; SPARQL; Structured data; Theory and Formal Methods; Validation constraints; Viral diseases; Wikidata; World Wide Web and Web Science; New York; Germany
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1085

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.