4033382
doi
10.5281/zenodo.4033382
oai:zenodo.org:4033382
user-covid-19
user-africarxiv
Mohamed Ali Hadj Taieb
Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
Thomas Shafee
La Trobe University, Melbourne, Victoria, Australia
Tiago Lubiana
Computational Systems Biology Laboratory, University of São Paulo, São Paulo, Brazil
Dariusz Jemielniak
Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Poland
Mohamed Ben Aouicha
Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
Jose Emilio Labra Gayo
Web Semantics Oviedo (WESO) Research Group, University of Oviedo, Spain
Mus'ab Banat
Faculty of Medicine, Hashemite University, Zarqa, Jordan
Diptanshu Das
Institute of Child Health (ICH), Kolkata, India
Daniel Mietchen
School of Data Science, University of Virginia, Charlottesville, Virginia, United States of America
Representing COVID-19 information in collaborative knowledge graphs: a study of Wikidata
Houcemeddine Turki
Faculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Public health surveillance
Wikidata
Knowledge graph
COVID-19
SPARQL
Community curation
FAIR data
Linked Open Data
<p>Information related to the COVID-19 pandemic ranges from biological to bibliographic and from geographical to genetic. Wikidata is a vast interdisciplinary, multilingual, open collaborative knowledge base of more than 88 million entities connected by well over a billion relationships and is consequently a web-scale platform for broader computer-supported cooperative work and linked open data. Here, we introduce four aspects of Wikidata that make it an ideal knowledge base for information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The structure of the raw data is highly complex, so converting it to meaningful insight requires extraction and visualization, the global crowdsourcing of which adds both additional challenges and opportunities. The created knowledge graph for COVID-19 in Wikidata can be visualized, explored and analyzed in near real time by specialists, automated tools and the public, for decision support as well as educational and scholarly research purposes via SPARQL, a semantic query language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format.</p>
<p>This paper is a preprint and has not yet received peer-review.</p>
Zenodo
2020-09-14
info:eu-repo/semantics/preprint
4028482
user-covid-19
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https://zenodo.org/records/4033382/files/Representing COVID-19 information in collaborative knowledge graphs_ a study of Wikidata (1).pdf
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