A method based on ontologies to support the automatic management of knowledge about Covid-19

The importance of integration and retrieval of data is growing more and more in the context of post-COVID-19 analysis because of the increased generation of data in research and resources for studying COVID-19. In this context, the analysis of contagion groups that share some specific feature (for example, people that work together, people with the same clinical manifestation, etc.) may offer remarkable insights about COVID-19. For instance, it can be helpful to recognize the behavior of this disease in accordance with the unique characteristics of a group of people. In this regard, ontologies are a widely accepted alternative for representing and analyzing knowledge. Given their benefits, this paper introduces an ontology-based method to formally describe and analyze information about specific groups of people with COVID-19. Since the ontology was specified in OWL, a formal language based on description logics, it enables the consistency of the represented information to be verified and use automatic reasoning to deduce new knowledge. This approach allows modeling a wide variety of characteristics (symptoms, comorbidities, treatments, etc.) of the individuals and consequently use it to infer the general characteristics of the groups that they belong to. Hence, a reasoner can be applied to perform advanced analysis either to identify patterns in these groups or to find similarity with other groups. To demonstrate the applicability of this method, a case study is described. In addition, the ontology was used to represent and analyze the information of a sample of patients extracted from a public dataset. The results demonstrate the capability of the ontology to represent and analyze information about specific groups of people with COVID-19.