Representing COVID-19 information in collaborative knowledge graphs: a study of Wikidata

Tracking #: 2572-3786

This paper is currently under review
Authors: 
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

Responsible editor: 
Armin Haller

Submission type: 
Full Paper
Abstract: 
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.
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Under Review