抽象的

Enhancing Social Personalized Search Based on Semantic Search Log using Ontology

K.Maheswari , Dr.S.Kirubakaran

As the Information available in the Internet is vast, the search engine provides search results based on page ranks. But the search results are not related to one particular user’s environment. But it is possible to provide customized search to each user with semantic technologies. Semantic Web is to add semantic annotation to the Web documents in order to access knowledge instead of unstructured material, allowing knowledge to be managed in an automatic way. A system called as Semantic Search log Social Personalized Search would be able to provide results for search query that relates to a particular user’s environment, the data that the user might have found to be useful while searching. In this system, supervised learning technique is used to learn about the user. Semantic web search is applicable for each and every registered user in this application. In the proposed work, ontology search logs are used, which will be used for providing customized search logs according to the user defined input.