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  4. The COVID-19 Ontology
 
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2020
Journal Article
Title

The COVID-19 Ontology

Abstract
Motivation The COVID-19 pandemic has prompted an impressive, worldwide response by the academic community. In order to support text mining approaches as well as data description, linking and harmonization in the context of COVID-19, we have developed an ontology representing major novel coronavirus (SARS-CoV-2) entities. The ontology has a strong scope on chemical entities suited for drug repurposing, as this is a major target of ongoing COVID-19 therapeutic development. Results The ontology comprises 2.270 classes of concepts and 38.987 axioms (2622 logical axioms and 2434 declaration axioms). It depicts the roles of molecular and cellular entities in virus-host interactions and in the virus life cycle, as well as a wide spectrum of medical and epidemiological concepts linked to COVID-19 . The performance of the ontology has been tested on Medline and the COVID-19 corpus provided by the Allen Institute. Availability COVID-19 Ontology is released under a Creative Commons 4.0 License and shared via https://github.com/covid-19-ontology/covid-19. The ontology is also deposited in BioPortal at https://bioportal.bioontology.org/ontologies/COVID-19.
Author(s)
Sargsyan, Astghik  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Kodamullil, Alpha Tom
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Baksi, Shounak
Darms, Johannes
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Madan, Sumit  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Gebel, Stephan
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Keminer, Oliver  
Jose, Geena Mariya
Balabin, Helena
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
DeLong, Lauren Nicole
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Kohler, Manfred
Jacobs, Marc  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Hofmann-Apitius, Martin  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Journal
Bioinformatics  
Project(s)
MAVO
Funder
Fraunhofer-Gesellschaft FhG
Open Access
DOI
10.24406/publica-r-265583
10.1093/bioinformatics/btaa1057
File(s)
Download (185.75 KB)
Rights
CC BY 4.0: Creative Commons Attribution
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Keyword(s)
  • interoperability

  • ontology and terminology

  • semantic integration

  • semantic interoperability

  • semantic search

  • text mining

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