SEReleC (Search Engine Result Refinement and Classification) - a Meta search engine based on combinatorial search and search keyword based link classification

The World Wide Web has immense resources for all kind of people for their specific needs. Searching on the Web using search engines such as Google, Bing, Ask have become an extremely common way of locating information. Searches are factorized by using either term or keyword sequentially or through short sentences. The challenge for the user is to come up with a set of search terms/keywords/sentence which is neither too large (making the search too specific and resulting in many false negatives) nor too small (making the search too general and resulting in many false positives) to get the desired result. No matter, how the user specifies the search query, the results retrieved, organized and presented by the search engines are in terms of millions of linked pages of which many of them might not be useful to the user fully. In fact, the end user never knows that which pages are exactly matching the query and which are not, till one check the pages individually. This task is quite tedious and a kind of drudgery. This is because of lack of refinement and any meaningful classification of search result. Providing the accurate and precise result to the end users has become Holy Grail for the search engines like Google, Bing, Ask etc. There are number of implementations arrived on web in order to provide better result to the users in the form of DuckDuckGo, Yippy, Dogpile etc. This research proposes development of a Meta search engine, called SEReleC that will provide an interface for refining and classifying the search engines´ results so as to narrow down the search results in a sequentially linked manner resulting in drastic reduction of number of pages.