A protocol for adding knowledge to Wikidata, a case report

Andra Waagmeester; Egon L. Willighagen; Andrew I. Su; Martina Kutmon; Jose Emilio Labra Gayo; Daniel Fernández-Álvarez; Peter J. Schaap; Lisa M. Verhagen; Jasper J. Koehorst

The WikiPathways use case shows us that literature describes our knowledge about how coronaviruses work at a rather detailed level. Indeed, many articles discuss the genetics, homology of genes and proteins across viruses, or the molecular aspects of how these proteins are created and how they interfere with the biology of the human cell. The biological pathways show these processes, but ultimately the knowledge comes from literature. Wikidata allows us to link literature to specific virus proteins and genes, depending on what the article describes. For this it uses the 'main subject' property ( P921 ). We manually annotated literature with the Wikidata items for specific proteins and genes. We developed two SPARQL queries to count the number of links between genes ( https://w.wiki/Lsp ) and proteins ( https://w.wiki/Lsq ) and the articles that discuss them. Scholia takes advantage of the 'main subject' annotation, allowing the creation of "topic" pages for each protein. For example, Figure 8 shows the topic page of the SARS-CoV-2 spike protein. Wikidata provides a solution. It is part of the semantic web, taking advantage of its reification of the Wikidata items as RDF. Data in Wikidata itself is frequently, often almost instantaneously, synchronised with the RDF resource and available through its SPARQL endpoint ( http://query.wikidata.org ). The modelling process turns out to be an important aspect of this protocol. Wikidata contains numerous entity classes as entities and more than 7000 properties which are ready for (re-)use. However, that also means that one is easily lost. The ShEx schema have helped us develop a clear model, as a social contract between the authors of this paper, as well as documentation for future users.