A protocol for adding knowledge to Wikidata, a case report

Abstract

Pandemics, even more than other medical problems, require swift integration of knowledge. When caused by a new virus, understanding the underlying biology may help finding solutions. In a setting where there are a large number of loosely related projects and initiatives, we need common ground, also known as a “commons”. Wikidata, a public knowledge graph aligned with Wikipedia, is such a commons and uses unique identifiers to link knowledge in other knowledge bases However, Wikidata may not always have the right schema for the urgent questions. In this paper, we address this problem by showing how a data schema required for the integration can be modelled with entity schemas represented by Shape Expressions. As a telling example, we describe the process of aligning resources on the genomes and proteomes of the SARS-CoV-2 virus and related viruses as well as how Shape Expressions can be defined for Wikidata to model the knowledge, helping others studying the SARS-CoV-2 pandemic. How this model can be used to make data between various resources interoperable, is demonstrated by integrating data from NCBI Taxonomy, NCBI Genes, UniProt, and WikiPathways. Based on that model, a set of automated applications or bots were written for regular updates of these sources in Wikidata and added to a platform for automatically running these updates. Although this workflow is developed and applied in the context of the COVID-19 pandemic, to demonstrate its broader applicability it was also applied to other human coronaviruses (MERS, SARS, Human Coronavirus NL63, Human coronavirus 229E, Human coronavirus HKU1, Human coronavirus OC4).

WikiPathways: connecting communities

“…Currently, the WikiPathways COVID-19 portal ( http://covid.wikipathways.org , see Figure 3 ) contains a collection of eleven molecular pathways on SARS-CoV-2 itself, nine on other coronaviruses from earlier outbreaks, and several known processes involving ACE2, the main target membrane enzyme of SARS-CoV-2 for entering host cells. Identifiers and cross-references for coronavirus genes and proteins are provided through a Wikidata project ( 19 ). Our pathway models are regularly updated and integrated into the COVID-19 Disease Map.…”

“…Like Wikipedia, Wikidata is a knowledge-sharing platform open to all (humans and software). In collaboration with the Gene Wiki and Reactome teams, we developed bots to add information about the curated pathways to Wikidata ( 19 , 32 ). The WikiPathways bot creates Wikidata items for each pathway and the content thereof in WikiPathways and aligns those with the Wikidata items on associated genes, proteins, metabolites, literature citations, and ontology annotations (e.g.…”

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

“…Second, COVID-19-related knowledge, while very limited at the start of the pandemic, was still embedded in a broader set of knowledge (e.g. about viruses, viral infections, past disease outbreaks and interventions), and these relationships -which knowledge bases are meant to leverage -are growing along with the expansion of our COVID-19 knowledge [15]. Third, the COVID-19 pandemic has affected almost every aspect of our globalized human society, so knowledge bases capturing information about it need to reflect that.…”

“…For instance, when the paper "Recent advances in the detection of respiratory virus infection in humans" was published on 2020-01-15, the item Q82838328 about it had been linked to the "SARS-CoV-2" item within less than three days: https://w.wiki/3XAt . health interventions, vaccine development and relevant publications -ready to be leveraged to explore the growing COVID-19-related knowledge in such broader contexts [15]. Third, both the Wikidata platform and the Wikidata community are highly multifaceted, multilingual and multidisciplinary [26,27].…”

Model-driven design of Mycoplasma as a vaccine chassis

“…The Pfam database is a large collection and classification of protein families that includes their annotations and MSA, generated using HMMs 276 . Pfam can be used to search specific proteins, to identify new targets for structure determination, to analyse amino acid sequences, to build phylogenetic trees or for functional annotation of genomic data [277][278][279][280][281][282][283][284][285] . Sometimes, protein families can show differences in the conserved domain regions: van Dam explains how to evaluate predicted occurrences of protein domains and their associated E-values, which might show a bimodal distribution, i.e.…”