A Personalized Context-Dependent Web Search Engine Using Word Net (Sama Search Engine)

Abstract

It is a fact that the growth of information resources on the WWW is increasing at every moment. So, it has increasingly become difficult for users to find information satisfies their individual needs. The current search engines are still immature to serve the exact desires of the enormous different users. One problem is that they do not consider the context of queries during searching process. Because of that, a lot of non-relevant results may be retrieved. In this work, we propose a personalized context-dependent web search engine model titled “Sama Search engine”. Various steps are involved in the approach: search results collection, preprocessing submitted query and collected results, concepts extraction, match and index results, and rank the retrieved results according to match and index flags finally. The main difference between the proposed model (Sama Search engine) and other Personalized Context-Dependent Search Engine models that it provides a new algorithm for matching and indexing the concepts extracted from both the submitted query and returned results. Also, effectiveness measures are used to evaluate the search engine. The F-measure obtained by the proposed model achieves with 99.35%. A comparative study between our proposed model and Semantic Tree (ST) model has been conducted. The results show that our proposed system outperforms the ST model.