Conceptual Cluster-based Large-scale Ontology Compression Approach
Feng Yang, Qin Ziting · International Journal of Database Theory and Application · 2016
With the development of semantic web and ontology application, there is a large number of ontology whose scale is large and the structure is complex in different fields. The existing mapping method and mapping system perform well when dealing with the mapping between a lightweight small ontology. However, when comes to the large-scale ontology, it is full of challenges to the methods and systems。To this end, this paper proposes a method of ontology compression based on conceptual cluster to compress. Firstly, it calculates the semantic similarity and semantic correlation of ontology concepts with the DICE coefficient method and the information entropy technology to get semantic relation. Secondly according to the semantic relations, it carries on the conceptual cluster in the space so that the of semantic relations closely together in groups. The of cluster in space is reduced, and the noise concept which is independent of the mapping is removed, and the purpose of the large-scale ontology compression is realized. Experimental results show that the method is so effective that it can compress the volume of large-scale ontology in the mapping problems.