Ontology Algorithm Using Two Classes of Regressions
Keywords:
ontology, ontology mapping, support vector regression, reproducing kernel Hilbert space, , -insensitive loss, Mercer kernel, regularization parameter, Huber loss function, Pseudo-Huber loss functionAbstract
Ontology similarity calculation is an important research topic in information retrieval and it is also widely used in
computer science. In this paper, we propose new algorithms for ontology similarity measurement and ontology mapping using
support vector regression and pseudo-Huber regression. Two experimental results show that the proposed new algorithm has
high accuracy and efficiency on ontology similarity calculation and ontology mapping.
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