OntoCOVID: Ontology for Semantic Modeling of COVID19 Statistical Data

ABSTRACT

Several COVID19 statistical datasets are provided to support stakeholders for better planning and decision making in healthcare. However, the datasets are in heterogeneous proprietary formats that create data silos and compatibility issues and make data discovery and reuse difficult. Further, the data integration for analysis is difficult and is performed by the domain experts manually which is time consuming and error prone. Therefore, an explicit, flexible, and widely acceptable methodology is needed to represent, store, query, and visualize COVID19 statistical data in the datasets. In this paper, we have presented the design and development of OntoCOVID ontology for representing, organizing, sharing, and reusing COVID19 statistical data in the datasets. The OntoCOVID is a lightweight ontology providing definitions of classes, properties, and axioms to semantically represent and relate information in the COVID19 statistical datasets. The OntoCOVID is evaluated to demonstrate its completeness and information retrieval for different use-case scenarios. The results obtained are promising and advocate for the improved ontological design and applications of the OntoCOVID. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.