Ontology Learning Algorithm via WMW Optimization Model

Abstract

Ontology, as a powerful semantic model, has been applied in various fields of computer science and information technologies such as image retrieval, knowledge management, retrieval extension, data integration, and knowledge extraction etc. Furthermore, it was used in other disciplines and raised great attention among the scholars. The purpose of ontology engineering applications is to get the similarity between ontology concepts, and the essence of ontology mapping is also similarity measuring since the map is constructed according to the similarity computation between different ontologies. In this paper, we report a new ontology learning technology using the Wilcoxon-Mann-Whitney optimizing model in which the ontology functions are assumed to be linear, and the fast version ontology learning algorithm is manifested as well. The results from four simulation studies show that the newly proposed algorithm has high efficiency and accuracy in ontology similarity measuring and ontology mapping in biology science, plant science, humanoid robotics and education science.