Abstract
As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or user local information. A personalized search engine which is a hybrid system has been proposed for personalized search and reasoning over user profiles. The hybrid system contains both the global knowledge base and the user local information. User profiles are created for every users to gather their interest and relevance. This system also learns user profiles based on which the user is provided with candidates in Ontology Learning Environment (OLE) tool. The search results are personalized and have topic specificity. The efficiency of a search application is defined by the accuracy by which the search results match the user interests. The efficiency improvement in our application can be defined by the reduction in the number of pages which a particular user who searches for a string. This search engine is evaluated by getting feedback from three kinds of users. The results show that this ontology model is successful.
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