Using logical constraints to validate information in collaborative knowledge graphs: a study of COVID-19 on Wikidata
- 1. Faculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia
- 2. Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Poland
- 3. Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
- 4. Web Semantics Oviedo (WESO) Research Group, University of Oviedo, Spain
- 5. Faculty of Medicine, Hashemite University, Zarqa, Jordan
- 6. La Trobe University, 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, India
- 10. School of Data Science, University of Virginia, Charlottesville, Virginia, United States of America
Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides a forum for exchanging structured data. In this research paper, we catalog the rules describing relational and statistical COVID-19 epidemiological data and implement them in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods to evaluate structured information, particularly COVID-19 knowledge in Wikidata, and consequently in collaborative ontologies and knowledge graphs, and we show the advantages and drawbacks of our proposed approach by comparing it to other methods for validation of linked web data.
This paper is a preprint and has not yet received peer-review.
Using logical constraints to validate information in collaborative knowledge graphs_ a study of COVID-19 on Wikidata.pdf
Using logical constraints to validate information in collaborative knowledge graphs_ a study of COVID-19 on Wikidata.pdfmd5:3734bf18a0f8c00dc4231cb3553e2a6f
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