  This comprehensive study conducts an in-depth analysis of existing COVID-19
ontologies, scrutinizing their objectives, classifications, design
methodologies, and domain focal points. The study is conducted through a
dual-stage approach, commencing with a systematic review of relevant literature
and followed by an ontological assessment utilizing a parametric methodology.
Through this meticulous process, twenty-four COVID-19 Ontologies (CovOs) are
selected and examined. The findings highlight the scope, intended purpose,
granularity of ontology, modularity, formalism, vocabulary reuse, and extent of
domain coverage. The analysis reveals varying levels of formality in ontology
development, a prevalent preference for utilizing OWL as the representational
language, and diverse approaches to constructing class hierarchies within the
models. Noteworthy is the recurrent reuse of ontologies like OBO models (CIDO,
GO, etc.) alongside CODO. The METHONTOLOGY approach emerges as a favored design
methodology, often coupled with application-based or data-centric evaluation
methods. Our study provides valuable insights for the scientific community and
COVID-19 ontology developers, supplemented by comprehensive ontology metrics.
By meticulously evaluating and documenting COVID-19 information-driven
ontological models, this research offers a comparative cross-domain
perspective, shedding light on knowledge representation variations. The present
study significantly enhances understanding of CovOs, serving as a consolidated
resource for comparative analysis and future development, while also
pinpointing research gaps and domain emphases, thereby guiding the trajectory
of future ontological advancements.
