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

Dear all,

I thank you for your efforts to provide access to FAIR data about COVID-19 pandemic. I am honoured to share with you our new research preprint entitled "Representing COVID-19 information in collaborative knowledge graphs: a study of Wikidata". This paper introduces Wikidata as a large-scale and collaborative multidisciplinary knowledge graph and provides details of how the flexible data model of Wikidata and its freely open and collaborative editing model can allow the construction and maintenance of FAIR COVID-19 data. This output also explains how SPARQL can be used to process and explore this interesting data to generate hidden insights about the infectious disease. This paper is available at https://doi.org/10.5281/zenodo.4028482 and has been sent for review. This work is complementary to another publication available at https://doi.org/10.5281/zenodo.4008358 explaining how multidisciplinary COVID-19 information on open and collaborative knowledge graphs can be validated and maintained using logical constraints. The figure included in the paper are made available for reuse under CC BY 4.0 License at https://commons.wikimedia.org/wiki/Category:COVID-19_Study_of_Wikidata. The SPARQL queries used in the research work have been provided under CC BY 4.0 License at https://www.wikidata.org/wiki/Wikidata:WikiProject_COVID-19/Queries/SPAR...<https://www.wikidata.org/wiki/Wikidata:WikiProject_COVID-19/Queries/SPARQL_Study>. I will be honoured to receive comments and discussions on the two research papers, particularly the one representing multidisciplinary COVID-19 information in the Wikidata knowledge graph.

Yours Sincerely,

Houcemeddine Turki