Representing COVID-19 information in collaborative knowledge graphs: the case of Wikidata

Tracking #: 2736-3950

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
Eric A. Youngstrom
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, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. A web-scale platform for broader computer-supported cooperative work and linked open data, it can be queried in multiple ways in near real time by specialists, automated tools and the public, including via SPARQL, a semantic query language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored and analyzed, for purposes like decision support as well as educational and scholarly research.
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