Ahmad, A. and Bandara, M. and Fahmideh, M. and Proper, H.A. and Guizzardi, G. and Soar, J. (2021) An overview of ontologies and tool support for COVID-19 analytics. In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW) :. IEEE. ISBN 9781665444897
Full text not available from this repository.Abstract
Context: The outbreak of the SARS-CoV-2 pandemic of the new COVID-19 disease (COVID-19 for short) demands empowering existing medical, economic, and social emergency backend systems with data analytics capabilities. An impediment in taking advantages of data analytics in these systems is the lack of a unified framework or reference model. Ontologies are highlighted as a promising solution to bridge this gap by providing a formal representation of COVID-19 concepts such as symptoms, infections rate, contact tracing, and drug modelling. Ontology-based solutions enable the integration of diverse data sources that leads to a better understanding of pandemic data, management of smart lockdowns by identifying pandemic hotspots, and knowledge-driven inference, reasoning, and recommendations to tackle surrounding issues.Objective: This study aims to investigate COVID-19 related challenges that can benefit from ontology-based solutions, analyse available tool support, and identify emerging challenges that impact research and development of ontologies for COVID-19. Moreover, reference architecture models are presented to facilitate the design and development of innovative solutions that rely on ontology-based solutions and relevant tool support to address a multitude of challenges related to COVID-19.Method: We followed the formal guidelines of systematic mapping studies and systematic reviews to identify a total of 56 solutions – published research on ontology models for COVID-19 – and qualitatively selected 10 of them for the review.Results: Thematic analysis of the investigated solutions pinpoints five research themes including telehealth, health monitoring, disease modelling, data intelligence, and drug modelling. Each theme is supported by tool(s) enabling automation and user-decision support. Furthermore, we present four reference architectures that can address recurring challenges towards the development of the next generation of ontology-based solutions for COVID-19 analytics.