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

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journal contribution
posted on 30.03.2022, 22:53 by H Turki, MAH Taieb, Thomas ShafeeThomas Shafee, T Lubiana, D Jemielniak, MB Aouicha, JEL Gayo, EA Youngstrom, M Banat, D Das, D Mietchen, A Haller
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.

History

Publication Date

03/02/2022

Journal

Semantic Web

Volume

13

Issue

2

Pagination

(p. 233-264)

Publisher

IOS Press

ISSN

1570-0844

Rights Statement

© 2022 – The authors. Published by IOS Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).