SEReleC# - C# implementation of SEReleC: a meta search engine based on combinatorial search and search keyword based link classification

• Authors: Vishwas Raval;Padam Kumar;Yogesh Kosta
• Venue: Proceedings of the CUBE International Information Technology Conference
• Year: 2012

The World Wide Web (WWW) has immense resources for all kind of people for their specific needs. Using search engines (e.g. Google, Bing, Yahoo!) to locate Web information is probably the most common application we use every day. However, the existing search engines suffer from certain drawbacks. First, searches are carried out by entering one or more relevant keywords or a short sentence. The challenge for the user is to come up with a set of search keywords or 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. Second, irrespective of the way the user specifies the search query, the results returned by search engines are in terms of millions of pages of which most might not be useful to the user. In fact, the end user never knows which pages are exactly matching their query and which contain irrelevant results unless they are checked individually (which is actually impossible given the huge volume of returned results). Finally, the results are not classified based on the search keywords which will surely benefit the user. This research has proposed and developed a meta-search engine, SEReleC (Search Engine Result Refinement and Classification), which addresses the above challenges. It provides an interface for refining search engines' results by eliminating redundant and irrelevant ones and classifying the remaining results into separate categories based on a combination of the search keywords. SEReleC addresses and removes limitations of existing search and meta-search engines by using following two innovative techniques - search keyword based Combinatorial Exact Search and Link Classification. Users can save the classified results into the local computer for future references. Extensive experimentation has been done in live environments (using Google, Bing, Yahoo!, DuckDuckGo, Dogpile and Yippy), to show that SEReleC achieves its objectives in a time-efficient manner. This research is still incomplete in the form of image retrieval which is one of the most challenging issues currently. The work is in progress for the same.