Ontology learning algorithm for similarity measuring and ontology mapping using linear programming

In order to represent the semantics and concepts better, the ontology, as an efficient model, has penetrated into all research areas of the computer science and information technology. In recent years, the ontology framework has also been applied to biology, chemistry, pharmaceutics, geography, and other fields, and it has attracted much attention from the researchers. Multi-dividing ontology algorithm is one of the popular learning approaches for ontology applications in which the vertex set and sample data are divided into several parts. In this paper, we introduce a new ontology optimization scheming by means of representer theorem and kernel function, and the method is a kind of linear programming. Four experiments are designed for the application of ontology algorithms in different fields to test the effectiveness by comparing the implement data.