10.5281/zenodo.4028483
https://zenodo.org/records/4028483
oai:zenodo.org:4028483
Houcemeddine Turki
Houcemeddine Turki
0000-0003-3492-2014
Faculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia
Mohamed Ali Hadj Taieb
Mohamed Ali Hadj Taieb
0000-0002-2786-8913
Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
Thomas Shafee
Thomas Shafee
0000-0002-2298-7593
La Trobe University, Melbourne, Victoria, Australia
Tiago Lubiana
Tiago Lubiana
0000-0003-2473-2313
Computational Systems Biology Laboratory, University of São Paulo, São Paulo, Brazil
Dariusz Jemielniak
Dariusz Jemielniak
0000-0002-3745-7931
Department of Management in Networked and Digital Societies, Kozminski University, Warsaw, Poland
Mohamed Ben Aouicha
Mohamed Ben Aouicha
0000-0002-2277-5814
Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia
Jose Emilio Labra Gayo
Jose Emilio Labra Gayo
0000-0001-8907-5348
Web Semantics Oviedo (WESO) Research Group, University of Oviedo, Spain
Mus'ab Banat
Mus'ab Banat
0000-0001-9132-3849
Faculty of Medicine, Hashemite University, Zarqa, Jordan
Diptanshu Das
Diptanshu Das
0000-0002-7221-5022
Institute of Child Health (ICH), Kolkata, India
Daniel Mietchen
Daniel Mietchen
0000-0001-9488-1870
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
Zenodo
2020
Public health surveillance
Wikidata
Knowledge graph
COVID-19
SPARQL
Community curation
FAIR data
Linked Open Data
2020-09-14
eng
10.5281/zenodo.4028482
https://zenodo.org/communities/covid-19
https://zenodo.org/communities/africarxiv
Creative Commons Attribution 4.0 International
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.