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  4. Representing COVID-19 information in collaborative knowledge graphs: The case of Wikidata
 
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2022
Journal Article
Title

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

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. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic 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.
Author(s)
Turki, H.
University Sfax Tunisia
Taieb, M.A.H.
University Sfax Tunisia
Shafee, T.
La Trobe University Melbourne
Lubiana, T.
University Sao Paolo Brazil
Jemielniak, D.
Kozminski University Warsaw
Aouicha, M.B.
University Sfax Tunisia
Labra Gayo, J.E.
University of Oviedo Spain
Youngstrom, E.A.
Uni of North Carolina Chapel Hill
Banat, M.
Hashemite University Zarqa Jordan
Das, D.
ICH Kolkata India
Mietchen, D.
Fraunhofer-Institut für Biomedizinische Technik IBMT  
Journal
Semantic web  
Open Access
File(s)
Download (1.57 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.24406/publica-r-270358
10.3233/SW-210444
Language
English
Fraunhofer-Institut für Biomedizinische Technik IBMT  
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