Title: Developing a fuzzy OWL ontology for obesity related cancer domain

Authors: Mohammad Abdelrahman Elhefny; Mohammed Elmogy; Ahmed A. Elfetouh; Farid A. Badria

Addresses: Information Systems Department, Faculty of Computers and Information, Mansoura University, Egypt ' Information Systems Department, Faculty of Computers and Information, Mansoura University, Egypt ' Information Systems Department, Faculty of Computers and Information, Mansoura University, Egypt ' Pharmacognosy Department and Liver Research Laboratory, Faculty of Pharmacy, Mansoura University, Egypt

Abstract: Obesity is associated with various diseases, particularly cardiovascular diseases, diabetes type 2, obstructive sleep apnea, certain types of cancer, osteoarthritis, and asthma. The knowledge of the obesity related cancer (ORC) domain is highly required to be represented in a structured and formalised shape. In this paper, we develop an ontology to represent ORC domain knowledge with its diseases, symptoms, diagnosis, and treatments. The proposed ontology is based on the Web Ontology Language (OWL 2) integrated with the fuzzy logic. The fuzzy developed ontology handles the overlapping concepts, ingesting more concepts, and copes with the linguistic domain variables, which were not possible using the regular ontologies. It allows the users to query the fuzzy Dl reasoner for element and answer them with the fuzzy ontology. By developing the fuzzy ORC ontology, it is expected to be a good practice for the ontologists and knowledge engineers.

Keywords: fuzzy ontology; obesity related cancer; ORC domain knowledge; Web Ontology Language 2; OWL2; knowledge representation; disease ontology; fuzzy logic.

DOI: 10.1504/IJMEI.2017.083092

International Journal of Medical Engineering and Informatics, 2017 Vol.9 No.2, pp.162 - 187

Received: 03 Sep 2015
Accepted: 20 Jun 2016
Published online: 20 Mar 2017 *

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