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Wikidata is a useful resource in the linked data network [1,2]. The Wikicite project has picked up the challenge to enter all scholarly publications (cited in Wikipedia) [3]. Inspired by Scholia [4], this electronic books collects a number of queries that we hope are useful to anyone who wants to learn more about the SARS-CoV-2 virus.

As will be clear when reading this booklet, a lot of queries take advantage of work by other, of the Wikidata:WikiProject COVID-19, of Wikicite, of the authors [5], and the selflessness efforts by many people around the world [6,7,8,9].

Knowledge graphs that specifically use data from Wikidata include the following:

Adding missing topic annotation

Because Wikidata is an open database, everyone can contribute, and one of the easiest ways to contribute, is annotate articles in Wikidata with ‘main subjects’. The content of the Literature chapter very much depends on this. If you want to help, get a Wikidata account, browse this book for mentioned literature and start adding ‘main subject’ annotation. This tutorial explains how to annotation literature with main subjects in Wikidata.

Other annotation

Furthermore, take advantage of the ‘/missing’ functionality of Scholia for other useful annotations. For example, check out these Scholia pages for the human coronaviruses (HCoVs):


  1. Vrandečić D. Wikidata: A New Platform for Collaborative Data Collection. Proceedings of the 21st International Conference on World Wide Web. 2012;1063–4. doi:10.1145/2187980.2188242 (Scholia)
  2. Waagmeester A, Stupp G, Burgstaller-Muehlbacher S, Good BM, Griffith M, Griffith O, et al. Wikidata as a knowledge graph for the life sciences. eLife. 2020 Mar 17;9. doi:10.7554/ELIFE.52614 (Scholia)
  3. Taraborelli D, Dugan JM, Pintscher L, Mietchen D, Neylon C. WikiCite 2016 Report [Internet]. Wikimedia Commons. 2016 Nov. Available from: doi:10.6084/M9.FIGSHARE.4042530 (Scholia)
  4. Nielsen FÅ, Mietchen D, Willighagen E. Scholia, Scientometrics and Wikidata. In: The Semantic Web: ESWC 2017 Satellite Events [Internet]. 2017. p. 237–59. Available from: doi:10.1007/978-3-319-70407-4_36 (Scholia)
  5. Waagmeester A, Willighagen EL, Su AI, Kutmon M, Gayo JEL, Fernández-Álvarez D, et al. A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses. BMC Biol [Internet]. 2021 Jan 22;19(1). Available from: doi:10.1186/S12915-020-00940-Y (Scholia)
  6. Turki H, Jemielniak D, Taieb MAH, Gayo JEL, Aouicha MB, Banat M, et al. Using logical constraints to validate information in collaborative knowledge graphs: a study of COVID-19 on Wikidata. Zenodo. 2020 Aug 30; doi:10.5281/ZENODO.4008359 (Scholia)
  7. Aouicha MB, Turki H, Taieb MAH, Shafee T, Lubiana T, Jemielniak D, et al. Representing COVID-19 information in collaborative knowledge graphs: a study of Wikidata. Zenodo. 2020 Sep 14; doi:10.5281/ZENODO.4033382 (Scholia)
  8. Waagmeester A, Willighagen E, Su AI, Summer-Kutmon M, Gayo JEL, Fernández-Álvarez D, et al. A protocol for adding knowledge to Wikidata, a case report. : the preprint server for biology. 2020. doi:10.1101/2020.04.05.026336 (Scholia)
  9. Reese JT, Unni D, Callahan TJ, Cappelletti L, Ravanmehr V, Carbon S, et al. KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response. Patterns. 2020 Nov 9;100155. doi:10.1016/J.PATTER.2020.100155 (Scholia)
  10. Darari F. COVIWD: COVID-19 Wikidata Dashboard. Jurnal Ilmu Komputer dan Informasi. 2021 Mar 1;14(1):39–47. doi:10.21609/JIKI.V14I1.941 (Scholia)