CovidO: an ontology for COVID-19 metadata

Ontology is a significant data model for identifying semantic information for Coronavirus disease (COVID-19) discovery. Large-scale biological and general datasets have recently been more readily available for study and to support the development of COVID-19 repositioning, but efficiently utilizing and standard metadata modeling of these datasets still remains challenging. Coronavirus disease is a deadly disease. Researchers have focused on the different aspects of COVID-19, such as studies about COVID-19, symptoms, treatment, prevention, resources, infected cases, patient information, global impact, and related research. Compilation and analysis of metadata by knowledge gathered in various field studies can help in efficient data sharing and better decision-making of data related to COVID-19. However, considering the heterogeneous nature of data sources, it is challenging and innovative to make the COVID-19 information human and machine understandable and to provide answers to user queries at all possible aspects of the COVID-19 pandemic. This paper propose a COVID-19 ontology named as CovidO metadata model that provides a common conceptual model to facilitate interoperability between metadata ontologies from various heterogeneous data sources. The CovidO has the following objectives: (1) The ontology serves as a reference schema for reporting COVID-19 data. (2) It also offer a customized schema of the existing ontologies to build a standard global data model. (3) It covers all the possible aspects/dimensions of COVID-19. (4) the proposed model aims to utilize findability, accessibility, interoperability, and reusability (FAIR) principles by providing common conceptualization metadata and thereby abstracting from heterogeneous structures of existing sources of (static) data. The experiments are conducted over CovidO using OOPS, OntoMetric, and RDF/SPARQL query to evaluate the efficiency of the proposed model. The result shows that the proposed model has a broad scope with fewer pitfalls than existing COVID-19 ontologies.