Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata-Reference-Cited by-同舟云学术

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

Published:2022-09-29 Issue: Volume:8 Page:e1085
ISSN:2376-5992
Container-title:PeerJ Computer Science
language:en
Short-container-title:

Author:

Turki Houcemeddine¹^ORCID,Jemielniak Dariusz²^ORCID,Hadj Taieb Mohamed A.¹^ORCID,Labra Gayo Jose E.³^ORCID,Ben Aouicha Mohamed¹,Banat Mus’ab⁴,Shafee Thomas⁵⁶^ORCID,Prud’hommeaux Eric⁷,Lubiana Tiago⁸,Das Diptanshu⁹¹⁰^ORCID,Mietchen Daniel¹¹¹²¹³¹⁴

Affiliation:

1. Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia

2. Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Masovia, Poland

3. Web Semantics Oviedo (WESO) Research Group, University of Oviedo, Oviedo, Asturias, Spain

4. Faculty of Medicine, Hashemite University, Zarqa, Jordan

5. La Trobe University, Melbourne, Victoria, Australia

6. Swinburne University of Technology, Melbourne, Victoria, Australia

7. World Wide Web Consortium, Cambridge, Massachusetts, United States of America

8. Computational Systems Biology Laboratory, University of São Paulo, São Paulo, Brazil

9. Institute of Child Health (ICH), Kolkata, West Bengal, India

10. Medica Superspecialty Hospital, Kolkata, West Bengal, India

11. Ronin Institute, Montclair, New Jersey, United States of America

12. Department of Evolutionary and Integrative Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany

13. School of Data Science, University of Virginia, Charlottesville, Virginia, United States

14. Institute for Globally Distributed Open Research and Education (IGDORE), Jena, Germany

Abstract

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.

Funder

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

Publisher

PeerJ

Subject

General Computer Science

Link

https://peerj.com/articles/cs-1085.pdf

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