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A community effort for COVID-19 Ontology Harmonization
Asiyah Yu Lin1, Yuki Yamagata2, William D Duncan3
1National Human Genome Research Institute, NIH, Bethesda, MD, USA.
CEUR Workshop Proceedings
|June 16, 2023
Summary
Harmonizing COVID-19 ontologies ensures data interoperability. This community effort merged four ontologies, demonstrating a feasible framework for integrative COVID-19 data analysis.
Area of Science:
- Biomedical Informatics
- Data Science
- Computational Biology
Background:
- Ontologies are crucial for managing complex biological data and knowledge.
- The COVID-19 pandemic generated vast amounts of data, necessitating specialized ontologies.
- Lack of standardization across COVID-19 ontologies hinders data integration and analysis.
Purpose of the Study:
- To harmonize disparate COVID-19 ontologies.
- To establish an interoperable framework for COVID-19 data representation.
- To facilitate integrated analysis of COVID-19 related data and knowledge.
Main Methods:
- Initiated a community-based collaborative effort involving developers of seven COVID-19 ontologies.
- Facilitated discussions for ontology harmonization.
- Merged four distinct COVID-19 ontologies into a unified structure.
Main Results:
- Successfully harmonized multiple COVID-19 ontologies.
- Demonstrated the feasibility of creating an interoperable ontology framework.
- Established a foundation for integrated representation and analysis of COVID-19 data.
Conclusions:
- Community-driven ontology harmonization is effective.
- An interoperable framework enhances COVID-19 data integration capabilities.
- Standardized ontologies are vital for advancing research and response to pandemics.
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