THE APPLICATION OF SEMANTIC INFORMATION CONTAINED IN RELEVANCE FEEDBACK IN THE ENHANCEMENT OF DOCUMENT RE-RANKING

Easily accessed publishing channels have resulted in the problem of information overload. Conventional information retrieval models, such as the vector model or the probability model, apply the lexical information contained in relevance feedback in the enhancement of document re-ranking. Improvement is possible considering the application of semantic information. Studies have been taking the approach of concept extraction and application in the dealing with this semantic matter. So far, a perfect solution remains elusive and research still has new ground to cover. As such, we have proposed and tested a strategic method to form a more understanding of this field of study. The results of formal tests show that the proposed method is more effective than the baseline ranking model

A Meta Search Engine Approach for Organizing Web Search Results using Ranking and Clustering

The web is expanding with each passing day along with technological advancement in search engine. This results in a long list of links to be retrieved for any user query. However it is not possible to verify each link of this returned list. Even the use of page ranking algorithms in searching does not provide the desired results. To address the solution to this problem a new meta search engine is introduced that uses the similarity measurement function to determine the relevancy of web page with the given query and document clustering technique to group the results into different clusters.