A HYBRID AND PERSONALIZED ONTOLOGY RANKING MODEL USING U-MEANS CLUSTERING AND HIT COUNT (Published)
Semantic Web is an extension of current Web which offers to add structure to the present Web. Ontologies play an important role in Semantic Web development and retrieval of relevant ontology. Ontology is being represented as a set of concepts and their inter-relationships relevant to some knowledge domain. As the number of Ontology repositories are more on Semantic Web, the problem of retrieving relevant ontologies of the scope arises. Even though there are Semantic Web search engines available, a major problem is that the huge number of results returned and which gives overhead to the searcher to find their need by themselves after going through the long list. This makes time consumption in search and creates dissatisfaction. One solution for this problem is that of maintaining the history of already analyzed, highly relevant and quality results in a log, which can used quickly to respond to the users of the similar type. This places highly relevant results analyzed and stored on the top list when results are presented to the searcher. Personalization and ranking takes care of these approaches. Another solution is the integration of clustering approach which helps in retrieving results from the history or log faster. This paper proposes a hybrid approach that creates the log and retrieves from log when the query is known and there are sufficient entries in the log. This approach imparts convenience to users and reduces the time complexity in finding their relevant needs.
Keywords: Clustering., Ontology, Ontology Ranking, Personalization, Semantic Search, Semantic Web