Ranking based ontology learning algorithm for similarity measuring and ontology mapping using representation theory

Abstract Ontology, as a semantic analysis and computation framework, has been widely used in various ways. Recently, learning algorithms have been employed for ontology similarity measuring and ontology mapping which aims to get a score ontology function and thus map each vertex on ontology graph to a real number. In this paper, ranking learning approach is employed in ontology function learning. The framework is designed based on reproducing kernel Hilbert space technology and representation theory trick. Finally, four experiments are manifested for the application of ontology algorithms in different fields. The effectiveness is proved by the comparison between data results.

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