Abstract

Several users use metasearch engines directly or indirectly to access and gather data from more than one data source. The effectiveness of a metasearch engine is majorly determined by the quality of the results it returns in response to user queries. The rank aggregation methods that have been proposed until now exploit a very limited set of parameters, such as total number of used resources and the rankings they achieved from each individual resource. In this paper, we use the fuzzy-bat to merge the score computation module effectively. Initially, we give a query to different search engines we use and the top n list from each search engine is chosen for further processing our technique. We then merge the top n list based on unique links, and we do some parameter calculations such as title-based calculation, snippet-based calculation, content-based calculation, address-based calculation, link-based calculation, uniform resource locator-based calculation, and co-occurrence-based calculation. We give the solutions of the calculations with the user given the ranking of links to the fuzzy-bat to train the system. The system then ranks and merges the links we obtain from different search engines for the query we give.