[Paritala* 5(1): January, 2018] ISSN 2349-4506 Impact Factor: 3.779 Global Journal of Engineering Science and Research Management http: // www.gjesrm.com © Global Journal of Engineering Science and Research Management [1] A STUDY ON IDENTIFYING DEFECTS AND SOLUTIONS OF SEARCH ENGINES Dr Chiranjeevi Paritala*, Dr Sai Monoj Kudaravalli * Associate Professor Dept. of CSE Amrita Sai Institute of Science and Technology Associate Professor Dept. of CSE Amrita Sai Institute of Science and Technology DOI: 10.5281/zenodo.1143969 KEYWORDS: Web Ontology Language (OWL), Personalization, SpyNB(NAÏVE BAYESIAN), Ontology based Multi-Facet (OMF),WKB (World Knowledge Base). ABSTRACT In internet, a wide range of web information increases rapidly, user wants to retrieve the information based upon his preference of using search engines. Our paper is going to propose a new type of search engine for web personalization approach. It will capture the interests and preferences of the user in the form of concepts of mining search results and their clickthroughs. Our approach is to improve the search accuracy by means of separating the concepts into content based concepts and location based which plays an important role in global search. Moreover, recognizing the fact that different users and queries may have different emphasis on content and location information, we introduce the content and location based concepts and achieves their respective results. Additionally, search engine also provides the facility of local search by entering keywords without using internet. And feature of integrity of the search engines at one location so that user can work with different search engines in parallel. INTRODUCTION From the last decade, there has been tremendous growth results are obtained from the backend search engines (e.g.Google, MSNSearch, and Yahoo). The search results are in the field of network. The information served to the combined and reranked according to the user's profile internet users through web is enormous. Some trained from the user's previous search activities. information provided is of use to the end users, and others Profile Updating: After the search results are obtained of no use to them. Current web information gathering systems attempt to satisfy user requirements by capturing from the backend search engines, the content and location their information needs. For this purpose, user profiles [5] concepts (i.e. important terms and phrases) and there are created for user background knowledge description. relationships are mined online from the search results and By capturing the users' interests in user profiles, a stored, respectively, as content ontology and location personalized search middleware is able to adapt the ontology. When the user clicks on a search result, the search results obtained from general search engines to the clicked result together with its associated content and users' preferences through personalized reranking [4] of location concepts are stored in the user's clickthrough the search results. The conceptual relationship between data. The content and location ontologies, along with the the documents has to be represented in order to identify clickthrough data, are then employed in RSVM [2] the information that a user wants from those represented training to obtain a content weight vector and a location concepts. To represent the semantic relation, the ontology weight vector for reranking the search results for the user. is used here. To build a user profile [5], the Web pages There is a number of challenging research issues we need that the user visited are monitored and the system to overcome in order to realize the proposed represents the long-term and short-term preference personalization approach. First, we aim at using concepts weights as the preference ontology after inferring relevant to represent and profile the interests of a user. Therefore, concepts from the general ontology. At the we need to build up and maintain a user's possible recommendation stage, the system recommends concept space, which are important concepts extracted from the user's search results. Additionally, we observe that location concepts exhibit different characteristics from content concepts and thus need to be treated differently. Thus, we propose to represent them in separate content and location ontologies. These ontologies not only keep track of the encountered concepts accumulated documents according to user preference concepts and document similarity measure.