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A protocol for adding knowledge to Wikidata: aligning resources on human coronaviruses.

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Summary
This summary is machine-generated.

Integrating diverse biological data for pandemics is crucial. This study uses Shape Expressions to model knowledge in Wikidata, enabling better data interoperability for urgent research questions, particularly for SARS-CoV-2.

Keywords:
COVID-19Linked dataOpen ScienceShExWikidata

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Knowledge Representation

Background:

  • Pandemics necessitate rapid knowledge integration, especially for novel viruses.
  • Wikidata, a public knowledge graph, serves as a common data resource but may lack specific schemas for urgent research needs.
  • Existing data integration methods often struggle with the dynamic and diverse nature of pandemic-related information.

Purpose of the Study:

  • To address the challenge of integrating diverse biological data for pandemic research.
  • To demonstrate how Shape Expressions can be used to define data schemas for Wikidata.
  • To enhance the interoperability of knowledge resources for studying viral pandemics.

Main Methods:

  • Modeling data schemas using Shape Expressions for Wikidata.
  • Aligning genomic and proteomic resources for SARS-CoV-2 and related viruses.
  • Integrating data from major biological databases like NCBI Taxonomy, NCBI Genes, UniProt, and WikiPathways.
  • Developing automated bots for regular updates within Wikidata.

Main Results:

  • Successfully defined Wikidata schemas using Shape Expressions for SARS-CoV-2 and related viral data.
  • Demonstrated data interoperability by integrating information from multiple public resources.
  • Developed and deployed automated bots for efficient and continuous data updates in Wikidata.
  • Established a robust workflow for pandemic data integration applicable to various human coronaviruses.

Conclusions:

  • The Shape Expressions approach provides a flexible and effective method for modeling complex biological data in Wikidata.
  • This workflow significantly improves data interoperability, crucial for rapid response during pandemics.
  • The developed system is broadly applicable beyond COVID-19, supporting research on other human coronaviruses.