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Along with the arrival of the era of large amount of data, many machine learning methods have been applied to the ontology similarity calculation and ontology mapping. In this paper, we raise an infinite push model for ontology similarity measuring and ontology mapping in multi-dividing setting. The iterative algorithm and generalization bound are given by virtue of the dual solution for optimization and the trick of covering number approach. Furthermore, the fast ontology algorithm for standard ontology SVM by virtue of infinite push multi-dividing ontology algorithm is obtained. Finally, four experiments presented on various fields verify the efficiency of the new computation model for ontology similarity measuring and ontology mapping applications in multi-dividing setting. [ABSTRACT FROM AUTHOR]

Ontology, as an effective structured information storage, classification, and statistical tool, has been attached importance from researchers of various disciplines. At present, ontology has become the core study field of semantic network, artificial intelligence, and information retrieval. The core topic of ontology algorithm is to calculate the similarity between concepts, so as to obtain the potential information of each other. In this paper, we present the boosting based iterative learning algorithm to compute the ontology sparse vector, and thus get the ontology function which maps each ontology concept to a real number. Since the advantage of ontology sparse vector is dimensionality reduction, the algorithm can extract the key information from the high dimensional ontology representation data. At last, we select four classical ontologies to test the efficiency of new ontology algorithm, and the rest give the positive answer. [ABSTRACT FROM AUTHOR]