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Research Article | Open Access |

Covid-19 Ontology Engineering-Knowledge Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)

Author 1: Vinu Sherimon Author 2: Sherimon P.C Author 3: Renchi Mathew Author 4: Sandeep M. Kumar Author 5: Rahul V. Nair Author 6: Khalid Shaikh Author 7: Hilal Khalid Al Ghafri Author 8: Huda Salim Al Shuaily
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 11, No. 11 · Published 2020 · Cited by 9

DOI: https://doi.org/10.14569/IJACSA.2020.0111115

Abstract

COVID-19 pandemic has rapidly spread across the world since its arrival in December 2019 from Wuhan, China. This pandemic has disrupted the health of the citizens in such a way that the impact is enormous in terms of economy and social aspects. Education, employment, income, well-being of the humankind is affected very crucially by this corona virus. Nations world-wide are struggling to battle this emergency. Intensive studies are being carried out to control this pandemic by researchers all over the world. Medical science has advanced a lot with the application of computer assisted solutions in health care. Ontology based clinical decision support systems (CDSS) assist medical practitioners in the diagnosis and treatment of diseases. They are well known in data sharing, interoperability, knowledge reuse, and decision support. This research article presents the development of ontology for SARS-CoV-2 (COVID-19) to be used in a CDSS, which is proposed in the satellite clinics of Royal Oman Police (ROP), Sultanate of Oman. The key concepts and the concept relationships of COVID-19 is represented using an ontology. Semantic Web Rule Language (SWRL) is used to model the rules related to the initial diagnosis of the patient and Semantic Query Enhanced Web Rule Language (SQWRL) is used to retrieve the data stored in the ontology. The developed ontology successfully classified the patients into one of the different categories as non-suspected, suspected, probable, and confirmed. The reasoning time and the query execution time is found to be optimal.

Keywords

How to Cite this Article

Sherimon, V., P.C, S., Mathew, R., Kumar, S. M., Nair, R. V., Shaikh, K., Ghafri, H. K. A., & Shuaily, H. S. A. (2020). Covid-19 Ontology Engineering-Knowledge Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). International Journal of Advanced Computer Science and Applications, 11(11). https://doi.org/10.14569/IJACSA.2020.0111115

Sherimon, Vinu, et al.. "Covid-19 Ontology Engineering-Knowledge Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)." International Journal of Advanced Computer Science and Applications, vol. 11, no. 11, 2020, https://doi.org/10.14569/IJACSA.2020.0111115.

@article{Sherimon2020,
  title     = {Covid-19 Ontology Engineering-Knowledge Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {11},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {Vinu Sherimon and Sherimon P.C and Renchi Mathew and Sandeep M. Kumar and Rahul V. Nair and Khalid Shaikh and Hilal Khalid Al Ghafri and Huda Salim Al Shuaily},
  doi       = {10.14569/IJACSA.2020.0111115},
  url       = {https://doi.org/10.14569/IJACSA.2020.0111115}
}

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