Ontology Optimization Algorithm for Similarity Measuring and Ontology Mapping Using Adjoint Graph Framework

As a semantic analysis and calculation model, ontology has been applied to many subjects. Numerous of machine learning approaches have been employed to the ontology similarity calculation and ontology mapping. In these learning settings, all information for a concept is formulated as a vector, and the dimension of such vector may be very large in certain special applications. To deal with these circumstances, dimensionality reduction tricks and sparse learning technologies are introduced in ontology algorithms. In this paper, we raise a new ontology framework for ontology similarity measuring and ontology mapping. We construct the adjoint ontology graph by means of index set of ontology vector. The optimal ontology vector is obtained in terms of Lagrangian relaxation approach. Finally, four experiments are presented from various perspectives of different fields to verify the efficiency of the new ontology framework for ontology similarity measuring and ontology mapping applications.