الفهرس 
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Abstract
Obesity has a tight relationship with increased risks of several types of cancers, such as Colorectal, Female Breast, Ovarian, Kidney (Renal-Cell), Adenocarcinoma, Pancreatic, Liver, and Gallbladder. It can also lead to some other diseases like diabetes and heart diseases. Yearly, obesity causes thousands of death cases around the world. The Obesity Related Cancer (ORC) is a rich and significant medical domain that needs its knowledge to be represented with its concepts, properties, and types of association. There was no Ontology made for ORC domain till moment, as far we know. There is a need to cope with overlapping concepts and uncertainty conditions in Ontology building. In this work, we propose building Obesity Related Cancer Ontology involving diseases, risk factors, symptoms, diagnosis, and treatment, using the latest standard Web Ontology language OWL 2. For better vocabularies specification, we applied the terms and structure of the standardized human’s disease Ontology (DOID)/(DO) to our Ontology terms and structure. Then, we integrated the fuzzy logic to the built Ontology by using the fuzzy annotation properties to solve the overlapping concepts and linguistic variables representation problems. It allows the users to query the Fuzzy Dl reasoner for element and get them back the fuzzy Ontology for that element. from our experiment, the proposed FOORC (Fuzzy Ontology for Obesity Related Cancer) was better to represent this domain than the typical one for several reasons. One of them was the ability to represent overlapping concepts and linguistic variables that had not sharp edges to be represented in regular Ontologies, and this was done via the Fuzzy annotation properties (like fuzzy datatypes, weighted sum concepts, …etc.). It led us to accommodate more concepts and make a wider range of vocabularies. Second, enabling the user(s) to send queries to the FuzzyDL reasoner that in turn replies with fuzzy Ontologies. Third, it best fits for rich domains having fuzzy knowledge that need to be represented within Ontologies. Finally, it led to a good performance, in general. We introduced a simple three phases methodology to build the FOORC that is expected to be good practice for Ontologists and knowledge engineers in medical field aiding them to solve the overlapping concepts, linguistic variables, and reasoning problems. Both physicians and intelligent systems can exploit obesity-related cancer Ontology in knowledge sharing, reusability, and reasoning.