A Semantic Approach for Classification of Web Ontologies

Semantic web provides virtual communities that enable intelligent interaction between software agents and people due to availability of standard open ontologies. But, as the semantic web is gaining much popularity, there is a massive growth seen in the ontology development which poses new research challenges such as ontology classification, ranking, searching, retrieval, etc. This results many recent developments, like OntoKhoj, Swoogle, OntoSearch, that facilitate user for such tasks. These semantic web portals mainly treat ontologies as plain texts and use traditional classification algorithms of plain text for classifying ontologies in directories and assigning predefined labels rather than using semantic knowledge hidden within the ontologies. These approaches suffer with many types of classification problems and lack of accuracy, especially in the case of overlapping ontologies that share common vocabularies. In this paper, we define ontology classification problem and categorized it into many sub-problems. We present a new methodology for ontology classification that is based on ontology approach for ontology classification and retrieval. The proposed framework, ONTCLASSIFIRE, benefit construction, maintenance or expansion of ontologies directories on the semantic web, and helps in ontology management and retrieval for software agents and people. We conclude that the use of context specific knowledge hidden in ontologies gives more accurate results of ontology classification and retrieval.