Ontology modularization method based on the K-PSO algorithm

In this paper, we focus on how to make use of K-PSO(Particle Swarm Optimization) algorithm to improve efficiency of the ontology modularization method. As a bridge of man-machine semantic interaction, ontology has an important status of logic basis of semantic realization and the upper expression of the metadata in the frame of the semantic web. But relative to the rich ontology resources, the application of specific network based on ontology auxiliary is relatively less. In order to solve this problem, we propose the ontology modularization method based on the K-PSO algorithm to make large-scale ontology play an important role in the organization of network information. Ontology modularization method proposed in this paper converts the problem of large-scale and intractable ontology semantic extraction into the internal dynamic extraction of ontology modules, and realizes the optimization semantic clustering of ontology modules. This method can effectively solve the actual application problems of ontology in the network information system based on semantic. Through a number of experiments, it shows that the method and framework to solve the problem in this paper is feasible, versatile and expansible. Compared with the classical clustering method, this method shows a better performance, which can use the particle swarm optimization algorithm to realize module clustering.