Apply Fuzzy Inference Mechanism for Supporting Healthcare Ontologies Management

Recently, owning to the fact that the numbers of patients suffering from the cardiovascular system (CVS) or respiratory diseases are growing progressively, healthcare is an increasingly important area. Therefore, in this paper, we apply the cosine measure process and Kullback-Leibler (KL) divergence approach to compute different probabilities that show how well one lexical entry in the Healthcare ontology related to another lexical entry in the Unified Medical Language System (UMLS) ontology. Besides, based on the cosine measure process and KL divergence value approach, we propose a fuzzy inference mechanism to infer the similarity between the healthcare ontology and UMLS ontology. Experimental results show that our approach can work effectively for evaluating similarity of these two ontologies.