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

Authors

  • Samia Allaoua-Chelloug Princess Nourah bint Abdulrahman University
  • Gilberto Fernando Castro Aguilar Universidad de Guayaquil and Universidad Católica de Santiago de Guayaquil https://orcid.org/0000-0001-9050-8550
  • Nemury Silega Southern Federal University https://orcid.org/0000-0002-8436-5650
  • Inelda Martillo Alcívar Universidad de Guayaquil,
  • Mohammed Saleh Ali Muthanna Tashkent state university of Economics
  • Ahmed Aziz Central Asian University https://orcid.org/0000-0003-1826-6248

Keywords:

Ontology, knowledge representation, Covid-19, automatic reasoning, decision making

Abstract

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.

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Author Biographies

Samia Allaoua-Chelloug, Princess Nourah bint Abdulrahman University

Samia Allaoua-Chelloug, received the Engineering degree in computer science, the master’s degree in computer science, and the Ph.D. degree in networking from the University of Constantine, Algeria, in 2003, 2006, and 2013, respectively. In August 2013, she joined the Department of Networks and Communication Systems, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, where she is currently an Assistant Professor. She has published more than 20 papers in journals and conference proceedings. Her current research interests include wireless sensor networks, body area networks, cloud computing, the Internet of Things, and pervasive computing.

Gilberto Fernando Castro Aguilar, Universidad de Guayaquil and Universidad Católica de Santiago de Guayaquil

Gilberto F. Castro Aguilar, undergraduate, postgraduate and doctoral studies at the Santiago de Guayaquil Catholic University (UCSG) / Guayaquil University (UG) / University of Informatics Sciences (UCI), works as Professor with the Faculty of Sciences Mathematics and Physics (UG) and with the Faculty of Engineering (UCSG). He has several publications in scientific journals.

Nemury Silega, Southern Federal University

Nemury Silega is graduated from the University of Informatics Sciences, Habana, Cuba, in 2007. He received the Ph.D. degree from the same university in 2014. He was as a Researcher with the Southern Federal University, Russia. His research interests fall within the fields related to model-driven development and ontology-driven engineering. He has published more than 50 papers in conferences and more than 20 in journals.

Inelda Martillo Alcívar, Universidad de Guayaquil,

Inelda Martillo Alcívar is a professor with more than 25 years of experience in Ecuador, graduated from the Catholic University of Santiago de Guayaquil with a degree in Computer Systems Engineering. Currently she is a professor-researcher at the University of Guayaquil. Her research interests are related to model-based development and ontology-based engineering. She has published more than 15 articles and has participated in several scientific conferences.

Mohammed Saleh Ali Muthanna , Tashkent state university of Economics

Mohammed Saleh Ali Muthanna received his M.S. degree from Saint Petersburg Electrotechnical University, Russia, in 2016, and the PhD from Chongqing University of Posts and Telecommunications, Chongqing, China in 2021. Currently, he is a postdoctoral fellow with the Southern Federal University, Russia. His main research interests include mobile edge computing, Software-Defined Networks (SDN), IoT, industrial wireless and sensor networks.

Ahmed Aziz, Central Asian University

Ahmed Aziz, received the B.Sc. degree (Hons.) in computer science and the M.S. degree in computer science from the Faculty of Computers and Informatics, Benha University, Benha, Egypt, in June 2007 and October 2014, respectively, and the Ph.D. degree in computer science from the School of Computer and System Science, Jawaharlal Nehru University, New Delhi, India, in 2019. Since October 2020, he has been a Professor with the Tashkent State University of Economics (TSUE), Tashkent, Uzbekistan. He has published more than 18 papers in journals. His research interests include sensor networks, compressive sensing, computing, wireless networks, and the IoT.

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Published

2025-10-01

How to Cite

Allaoua-Chelloug, S., Castro Aguilar, G. F., Silega, N., Martillo Alcívar, I., Ali Muthanna , M. S. ., & Aziz, A. . (2025). A method based on ontologies to support the automatic management of knowledge about Covid-19. IEEE Latin America Transactions, 23(11), 969–979. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/9707

Issue

Vol. 23 No. 11 (2025): Ordinary Issue

Section

Computing