Houcemeddine Turki
Mohamed Ali Hadj Taieb
Thomas Shafee
Tiago Lubiana
Dariusz Jemielniak
Mohamed Ben Aouicha
Jose Emilio Labra Gayo
Mus'ab Banat
Diptanshu Das
Daniel Mietchen
2020-09-14
<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>
https://doi.org/10.5281/zenodo.4033382
oai:zenodo.org:4033382
eng
Zenodo
https://zenodo.org/communities/covid-19
https://zenodo.org/communities/africarxiv
https://doi.org/10.5281/zenodo.4028482
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
Representing COVID-19 information in collaborative knowledge graphs: a study of Wikidata
info:eu-repo/semantics/preprint