Prototype for Enhancing Search Engine Performance Using Semantic Data Search

Abstract

Information’s on Internet are vast that are retrieved by the search engines based on page ranks. But the search results are not related to one particular user’s environment. Many researches had been possessed to provide better results.  In this project, we propose a new system called as Semantic Search log Social Personalized Search which would be able to provide results for search query that relates to a particular user’s environment based on the users area of interests, his likes and dislikes etc.., Social networks are such domain in which we could obtain the user oriented information, which can be used for providing personalized search results. Here a supervised learning technique is used to learn about the user, based upon his interactions inside the system. This process can be able to make applicable for each and every registered user in this application. This can be done by proving the user basic information in their profile and get benefits from their each and every search.

When the user gets register with the system, it creates an ontological profile, when the user gets login into the social network and interacts with it the system updates the user ontological profile based upon their interaction. The search provision can be finding out in their home page after they get login. When the user searches a keyword using the search engine inside the social network, it refers to the ontological profile of the user and displays the Personalized Search results. The system should be able to intelligently identify whether a search result has been useful to him or not and save it for his future reference when he searches for the same or similar keyword next time. The main objective of this project involves with search engine and its optimization methods. A new technique called as ontology search logs is introduced, which will be used for customized search logs according to the user’s define input based on  his/her area of interests, his/her likes and dislikes,. This application will be processed in any type of the search engine.