A Vertical Meta Search Engine with Query Expansion Using Artificial Relevance Feedback Mechanism

World Wide Web is growing day by day so with this rapid development in the size of internet, Information extraction[1] on Internet is gaining its importance day by day. At present there are millions of Websites and billions of homepages available on the Internet. A large amount of on-line information resides on the invisible web[2] – web pages generated dynamically from databases and other data sources hidden from the user. They are not indexed by a static URL but is generated when queries are asked via a search engine (we denote them as specialized search engines or vertical search engine). OpenFind states that it indexes 3.5 billion Web pages; Google claims 2.4 billion, AlltheWeb 2.1 billion, Inktomi a little more than 2 billion, WiseNut 1.5 billion and AltaVista 1 billion Web pages. No search engine index more than one third of the total size[3] of the web. So from this big collection of web pages information retrieval is a very crucial task. The user query plays a vital role in the information retrieval process. So for the better information retrieval results several methods have been devised which assists the user in the query expansion task. In the proposed system we present a Vertical Meta Search Engine with query expansion using Artificial Relevance feedback mechanism. The proposed system provides a simple way of query expansion based on relevance feedback and reduces the user’s searching time with less no of hits to get the accurate results.