Towards Classification of Web Ontologies for the Emerging Semantic Web

The massive growth in ontology development has opened new research challenges such as ontology management, search and retrieval for the entire semantic web community. These results in many recent developments, like OntoKhoj, Swoogle, OntoSearch2, that facilitate tasks user have to perform. These semantic web portals mainly treat ontologies as plain texts and use the traditional text classification algorithms for classifying ontologies in directories and assigning predefined labels rather than using the semantic knowledge hidden within the ontologies. These approaches suffer from 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 an ontology classification problem and categorize it into many sub-problems. We present a new ontological methodology for the classification of web ontologies, which has been guided by the requirements of the emerging Semantic Web applications and by the lessons learnt from previous systems. The proposed framework, OntClassifire, is tested on 34 ontologies with a certain degree of overlapping domain, and effectiveness of the ontological mechanism is verified. It benefits the construction, maintenance or expansion of ontology directories on the semantic web that help to focus on the crawling and improving the quality of search for the software agents and people. We conclude that the use of a context specific knowledge hidden in the structure of ontologies gives more accurate results for the ontology classification.