Aligning Ontologies Intelligently

In the semantic web, ontology plays an important role to provide formal definitions of concepts and relationships. Due to the presence of several similar ontologies in the same domain, there might be several definitions for a given concept. Ontology alignment overcomes these difficulties by exploring a map between similar entities that refer to the same concept in two different ontologies. This paper proposes a method to combine similarity measures of different categories such as string, linguistic, structural and instance based similarity measures. To align different ontologies efficiently, K Nearest Neighbor (KNN), Support Vector Machine (SVM) and Decision Tree (DT) classifiers and Baysian network are investigated. Each classifier is optimized based on the lower cost and better classification rate. Experimental results demonstrate that the F-measure criterion improves up to 98% using feature selection and combination of classifiers, which is highly comparable, and outperforms the previous reported F-measures.