Intelligent Information Retrieval

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Intelligent Information Retriever|
Inception of Artificial Intelligencein Search Engines|
Member Details Member Id.| Name | College| Email-id|
1)| Mitesh Mahadev Mangaonkar| Vidyalankar Institute of Technology|| 2)| Sushant sumbare| Vidyalankar Institute of Technology||

Intelligent Information Retriever
Inception of Artificial Intelligence
in Search Engines
Paul S. Rosenbloom, John E. Laird
A Book Proposal

Abstract- The World Wide Web has become an invaluable information resource but the explosion of information available via the web has made web search a time consuming and complex process. Index-based search engines, such as AltaVista, Google or Infoseek help, but they are not enough. This paper describes the rationale, architecture, and implementation of a next generation information gathering system – a system that integrates several areas of Artificial Intelligence (AI) research under a single umbrella. Our solution to the information explosion is an information gathering agent, IIR , that plans to gather information to support a decision process, reasons about the resource trade-offs of different possible gathering approaches, extracts information from both unstructured and structured documents, and uses the extracted information to refine its search and processing activities.

The World Wide Web has given the researchers, businessmen, corporate, students, hobbyists and technical groups a medium by which they can share the information they have, with others. The ease of HTML and platform independence of the web documents has lead to a tremendous growth of the web, that has outstripped the technologies that are used to effectively search in these pages, as well as proper navigation and interpretation.[1] With the aim of inception of AI (Artificial Intelligence) in the searching techniques, the first step we have decided is to find out those limitations in the current searching methodologies, which make the result unsatisfactory and not up to the expectations. Some of the key features of today's search engines are: * Meta Searching: The scope of each search engine is limited and no search engine has the database that covers all the web pages. This problem was noted long ago and was solved with the help of Meta search sites that make use of multiple search engines to search for the "Query String. The common names of such search engines are (which searches 37 search sites simultaneously), and many others. Another advantage of these Meta search sites is that they incorporate advanced features which are absent in some of the member search sites (Member search sites are those sites which return the search result to Meta Search engines). But the basic methods used in these Meta search sites are more or less same as those used in any other search engines.  

* URL Clustering: URL clustering was a basic problem from which most of the earlier search sites were affected. Suppose we search for 'GRE' and we intend to get the link to all those sites that have information on GRE exam. But a search engine without URL clustering will give results like: #1 (37k)

Result Summary: This is the official GRE site…
#2 (30k)
Result Summary: GRE can be given any…
#3 (76k)
Result Summary: …is the list of GRE exam centers…
As you can see, the results are all from the same site, defeating the purpose of a search engine. A site with URL clustering will give the results from other sites as well, with the option to have results from deeper pages. A typical such result would be: #1 (37k)

Result Summary: This is the official GRE site…
(more results from this site)
#2 (56k)
Result Summary: …sample CBTs on GRE…
(more results from this site)...
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