An Overview of Ontologies and Tool Support for COVID-19 Analytics

Ahmad, Aakash and Bandara, Madhushi and Fahmideh, Mahdi and Proper, Henderik A. and Guizzardi, Giancarlo and Soar, Jeffrey ORCID: (2021) An Overview of Ontologies and Tool Support for COVID-19 Analytics. In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), 25 Oct 2021, Gold Coast, Australia.


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.

Statistics for USQ ePrint 44036
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 06 Dec 2021 05:53
Last Modified: 21 Jan 2022 02:26
Uncontrolled Keywords: COVID-19, Ontology, Analytics, Semantic Web, Reference Architecture, Tool Support
Fields of Research (2008): 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460999 Information systems not elsewhere classified
Socio-Economic Objectives (2008): C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Socio-Economic Objectives (2020): 20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified
Identification Number or DOI:

Actions (login required)

View Item Archive Repository Staff Only