A Similarity-Based Concepts Mapping Method Between Ontologies

Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.