Abstract:
Every presence has needs. A need could be consumer electronics, loans, autos, or knowledge, etc. The presence uses search engines to become knowledgeable about the need. The current search engines are not intelligent. The idea of the invention is to provide more accurate results and semantically linked results through an intelligent index. The invention uses results from known, semantic and unknown indexes with Guiding Information to return the most relevant results to the users.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
   This application claims priority of U.S. provisional application, Ser. No. 60/653,349, titled “Intelligent Search With Guiding Info” filed Feb. 16, 2005 by the present applicant. 
   This application references U.S. patent application Ser. No. 11/162,735 titled “Using Popular IDs To Sign On Creating A Single ID For Access” filed Sep. 21, 2005 by the present applicant. 
   This application references U.S. application Ser. No. 11/161,897, titled “Obtaining A Need With Guiding Information And Credit Worthiness Using A Competitive Process” filed Aug. 22, 2005 by the present applicant. 
   This application refers to the U.S. provisional application No. 60/656,645, titled “Guiding Info Tabs With Guide Selection” filed Feb. 23, 2004 by the present applicant. 

   FIELD OF THE INVENTION 
   The present invention relates to searching on the internet or the intranet using the intelligence of the users on the net. 
   BACKGROUND OF THE INVENTION 
   Search engines never return what you want. They are good for general searches, but as your use advances, one seems to click on multiple pages, multiple links to get to the relevant information. Ranking by number of links has led to web masters linking with each other, increasing the number of links referencing a page, giving the page a higher rank. A higher rank allows the pages to be the first links in search results but the content might not be what the user is looking for. So when you search for a term as a mortgage loan or escaping xml, etc. you get results that might not be meaningful. 
   When a user searches for “Taco Bell” or “Panasonic TV” or “Laptop computer” or “need a mortgage loan”, the user is expecting to get information related to finding the restaurant “Taco Bell”, or buying a “Panasonic TV” or a “Laptop computer” or information about a “mortgage loan”. A user searching for “Panasonic TV” expects to see the popular models, models on sale, market competitive information, information about TVs, LCD TVs, etc. A user searching for a “Laptop computer” is interested in knowing more about a laptop computer, a computer, different models on sale, popular selections, market information, etc. For a “mortgage loan” or “need a mortgage loan” the user might be interested in, what is a mortgage loan?, types of loan products, current interest rates, different mortgage brokers, bankers, market competitive information, etc. A user searching for “relief from cough” or “tantrums 10 yr old” is looking to find information related to finding an immediate relief to the cough or how to overcome the tantrums thrown by the 10 yr old. The search engines with natural language ability do try to understand the context but again might not present the information needed by the user. 
   A definite need exists for an intelligent search engine with guiding information that could overcome the problems associated and described above. The idea is to use known information, semantic information and the intelligence of the users on the net to build an intelligent index that could present relevant information. Guiding Information, patent pending U.S. application Ser. No. 11/161,897 with Guide Selections provides expert information about a category and sub category along with market competitive information. So a user searching for a laptop computer can immediately become knowledgeable about buying a laptop and buy one online. A user searching for mortgage loans gets guiding information about mortgage, loans and can proceed on to buying one, while a user searching for “XML escaping”, will get information related to the problem of escaping XML. A primary purpose of the present invention is to solve these needs and provide further, related advantages. 
   BRIEF DESCRIPTION OF THE INVENTION 
   Every presence has needs. A need could be consumer electronics, loans, autos, etc. The presence uses search engines to start looking for information related to electronics like laptops, loans, autos, etc. According to Comscore, users typically start their buying process at the search engines with generic terms like “Icd tv” become knowledgeable and then proceed either to a store or buy online. Most searches engines build their indexes on keyword density, page ranking based on number of links, etc. None are intelligent today. The idea of the invention is to provide more accurate results and semantically linked results of what a user is looking for. The invention uses results from known, semantic and unknown indexes with Guiding Information to return the most meaningful and relevant results. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The accompanying drawings, which are incorporated into and constitute a part of this specification, illustrate one or more embodiments of the present invention and, together with detailed description, serve to explain the principles and implementations of the invention. 
       FIG. 1  is an illustration of a user searching for the term “xml escaping”. 
       FIG. 2  is an illustration of a second user searching for “xml escaping” something similar to what the first user was searching. 
       FIG. 3  is an illustration of the search results returned for user 3 . 
       FIGS. 4   a ,  4   b  are flow charts illustrating the index being created, links being added dynamically, and a walk through the search process. 
       FIG. 5  is a flowchart illustrating what happens when a user selects a link by clicking on it. 
       FIG. 6  is an illustration of the search results shown along with guiding info. 
       FIG. 7  is an illustration of the search results shown along with guiding info, a continuation of  FIG. 6 . 
     
       
         
               
             
               
               
               
               
             
           
               
                   
               
               
                 REFERENCES CITED 
               
               
                 U.S. patent documents 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 6,961,731 
                 November 2005 
                 Holbrook 
                 707/102 
               
               
                 5,511,186 
                 April 1996 
                 Carhart, et al. 
               
               
                 6,961,723 
                 November 2005 
                 Faybishenko, et al. 
                 707/3 
               
               
                 5,577,241 
                 November 2005 
                 Spencer 
                 707/5 
               
               
                 5,265,065 
                 November 2005 
                 Turtle 
                 707/4 
               
               
                 5,321,833 
                 July 1994 
                 Chang, et al. 
                 707/5 
               
               
                 5,659,732 
                 August 1997 
                 Kirsch 
                 707/5 
               
               
                 5,544,352 
                 August 1996 
                 Egger 
                 707/5 
               
               
                 5,920,854 
                 July 1999 
                 Kirsch, et al. 
                 707/3 
               
               
                 5,933,822 
                 August 1999 
                 Braden-Harder, et al. 
                 707/5 
               
               
                 6,070,158 
                 May 2000 
                 Kirsch, et al. 
                 707/3 
               
               
                 6,145,003 
                 November 2000 
                 Sanu, et al. 
                 709/225 
               
               
                 6,233,571 
                 May 2001 
                 Egger, et al. 
                 707/2 
               
               
                 6,256,623 
                 July 2001 
                 Jones 
                 707/3 
               
               
                 6,275,820 
                 August 2001 
                 Navin-Chandra, et al. 
                 707/3 
               
               
                 6,078,914 
                 July 2000 
                 Redfern 
                 707/3 
               
               
                 6,278,993 
                 August 2001 
                 Kumar, et al. 
                 707/3 
               
               
                 6,317,741 
                 November 2001 
                 Burrows 
                 707/5 
               
               
                 6,336,116 
                 January 2002 
                 Brown, et al. 
                 707/10 
               
               
                 6,434,548 
                 August 2002 
                 Emens, et al. 
                 707/3 
               
               
                 6,442,544 
                 August 2002 
                 Kohli 
                 707/5 
               
               
                 6,453,315 
                 September 2002 
                 Weissman, et al. 
                 707/5 
               
               
                 6,480,837 
                 November 2002 
                 Dutta; Rabindranath 
                 707/3 
               
               
                 6,484,166 
                 November 2002 
                 Maynard 
                 707/5 
               
               
                 6,490,575 
                 December 2002 
                 Berstis 
                 707/3 
               
               
                 6,523,026 
                 February 2003 
                 Gillis 
                 707/3 
               
               
                 6,961,723 
                 November 2005 
                 Faybishenko, et al. 
                 707/1 
               
               
                 6,526,400 
                 February 2003 
                 Takata, et al. 
                 707/3 
               
               
                 6,560,600 
                 May 2003 
                 Broder 
                 707/7 
               
               
                 6,665,655 
                 December 2003 
                 Warner, et al. 
                 707/2 
               
               
                 6,687,696 
                 February 2004 
                 Hofmann, et al. 
                 707/6 
               
               
                 6,718,324 
                 April 2003 
                 Edlund, et al. 
                 707/5 
               
               
                 6,757,675 
                 June 2004 
                 Aiken, et al. 
                 707/3 
               
               
                 6,763,362 
                 July 2003 
                 McKeeth 
                 707/104.1 
               
               
                 6,785,671 
                 August 2004 
                 Bailey, et al. 
                 707/3 
               
               
                 6,799,176 
                 September 2004 
                 Page 
                 707/5 
               
               
                 6,801,906 
                 October 2004 
                 Bates, et al. 
                 707/3 
               
               
                 6,947,936 
                 September 2005 
                 Suermondt, et al. 
                 707/7 
               
               
                 6,947,932 
                 September 2005 
                 Brandin, et al. 
                 707/6 
               
               
                 6,947,920 
                 September 2005 
                 Alpha 
                 707/1 
               
               
                 6,963,867 
                 November 2005 
                 Ford, et al. 
                 707/3 
               
               
                 6,950,814 
                 September 2005 
                 Bergan, et al. 
                 706/55 
               
               
                 6,801,906 
                 October 2004 
                 Bates, et al. 
                 707/3 
               
               
                 6,766,320 
                 July 2004 
                 Wang, et al. 
                 707/5 
               
               
                 6,766,316 
                 July 2004 
                 Caudill, et al. 
                 707/3 
               
               
                 6,728,704 
                 April 2004 
                 Mao, et al. 
                 707/3 
               
               
                 6,714,934 
                 March 2004 
                 Fordham 
                 707/100 
               
               
                 6,704,722 
                 March 2004 
                 Wang Baldonado 
                 707/3 
               
               
                 6,701,312 
                 March 2004 
                 Lau, et al. 
                 707/5 
               
               
                 6,578,022 
                 July 2003 
                 Foulger, et al. 
                 706/45 
               
               
                 6,490,577 
                 December 2002 
                 Anwar 
                 707/4 
               
               
                 6,278,992 
                 August 2001 
                 Curtis, et al. 
                 707/3 
               
               
                 5,987,446 
                 November 1999 
                 Corey, et al. 
                 707/3 
               
               
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
               
             
           
               
                   
               
               
                 U.S. patent applications 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 20050216434 
                 September 2005 
                 Haveliwala, Taher H.; et al. 
                 707/1 
               
               
                 20050120006 
                 June 2005 
                 Nye, Timothy G. 
                 707/3 
               
               
                 20050076021 
                 April 2005 
                 Wu, Yuh-Cherng; et al. 
                 707/3 
               
               
                 20050055340 
                 March 2005 
                 Dresden, Scott 
                 707/3 
               
               
                 20050004905 
                 January 2005 
                 Dresden, Scott 
                 707/3 
               
               
                 20040260688 
                 December 2004 
                 Gross, John N. 
                 707/3 
               
               
                 20040254917 
                 December 2004 
                 Brill, Eric D.; et al. 
                 707/3 
               
               
                 20040243568 
                 December 2004 
                 Wang, Hai-Feng; et al. 
                 707/3 
               
               
                 20040044657 
                 March 2004 
                 Lee, Jae-Hak 
                 707/3 
               
               
                 20040030690 
                 February 2004 
                 Teng, Albert Y.; et al. 
                 707/3 
               
               
                 20040024756 
                 February 2004 
                 Rickard, John Terrell 
               
               
                 20040010484 
                 January 2004 
                 Foulger, Michael G.; et al. 
                 706/50 
               
               
                 20030120653 
                 June 2003 
                 Brady, Sean; et al. 
                 707/7 
               
               
                 20020152199 
                 October 2002 
                 Teng, Albert Y.; et al. 
                 707/3 
               
               
                   
               
             
          
         
       
     
   

   OTHER REFERENCES 
   
       
       1. Gravano, et al., “STARTS: Stanford Proposal for Internet Meta-Searching,” 1997, ACM, XP000730508, pp. 207-218. 
       2. Gravano, Querying Multiple Document Collections Across The Internet, Phd. Thesis, 1998, UMI Order No. GAX98-10132. 
       3. The metasearch engines, http://www.evolt.org/article/The_Meta_Search_Engines/12/41694/, Oct. 9, 2002 
       4. SDLIP+STARTS=SDSTARTS, A Protocol and Toolkit for Metasearching, 2001, ACM, Pages: 207-214. 
       5. C. Yu, et al., “Efficient and Effective Metasearch for a Large No. of Text Databases,” Tech. report, U. of Illinois at Chicago, 1999, http://citeseer.ist.psu.edu/yu99efficient.html, 2 pages. 
       6. Walter L. Warnick, PhD, et al., “Searching the Deep Web,” D-Lib Magazine, January 2001, Vol. 7, No. 1, 11 pages. 
       7. Lucene API, http://lucene.apache.org/java/docs/index.html. 
     
  
   DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
   Embodiments of the present invention are described herein in the context of a method and apparatus for emulating a competitive process. Those of ordinary skill in the art will realize that the following detailed description of the present invention is illustrative only and is not intended to be in any way limiting. Other embodiments of the present invention will readily suggest themselves to such skilled persons having the benefit of the disclosure. Reference will now be made in detail to the implementations of the present invention as illustrated in the accompanying drawings. The same reference indicators will be used throughout the drawings and the following detailed description to refer to the same or like parts. 
   In the interest of clarity, not all of the routine features of the implementations described herein are shown and described. It will, of course, be appreciated that in the development of any such actual implementation, numerous implementation-specific decisions must be made in order to achieve the developer&#39;s specific goals, such as compliance with application—and business related constraints, and that these specific goals will vary from one implementation to another and from one developer to another. Moreover, it will be appreciated that such a development effort might be complex and time consuming, but would nevertheless be routine undertaking of engineering for those of ordinary skill in the art having the benefit of this disclosure. 
   In accordance with the present invention, the components, process steps, and/or data structures may be implemented using various types of operating systems, computing platforms, computer programs, and or/general purpose machines. In addition, those of ordinary skill in the art will recognize that devices of a less general purpose nature such as hardwired devices, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), or the like, may also be used without departing from the scope and spirit of the inventive concepts disclosed herein. 
   The purpose and idea of the invention is to use results returned from popular search engines, vertical portals, hidden web, intelligent indexing, Guiding Info, a patent pending, U.S. application Ser. No. 11/162,735, Guide Selections patent pending, provisional U.S. application No. 60/656,645 to present semantically correct and relevant results. 
   Search engines index web pages using automated robots or web spiders that traverse one link to another and build a database of these links. They analyze the content of the page for word relevance, number of links to the page, word density, meta tags, links to a particular page, etc. Some of them try to understand the context of the word being searched and try to show words relevant to the search context. 
   The idea of the invention is to use these search engines for what has already been built and use the returned results for intelligent indexing. The intelligent index could also use results from its own search engine. 
   The intelligent index is made up of multiple indexes, a known index comprising of known terms, a semantic index comprising of semantic relationships and directives, and an unknown index. The known index contains search terms such as “Taco Bell”, “Laptop computers”, “LCD TV”, “Laptop computer”, “Mortgage Loans”, “Transaxtions”, etc. The idea here is that if a user searches for “LCD TV” the results should be related to information about a “LCD TV”, what is a “LCD TV”, different models, popular selections, market competitive information, etc. allowing the user to get the information about buying a “LCD TV” or knowing all about LCD TVs. 
   The second index will be a semantic index which will hold terms and their relationships and the context in which they are being used. For example, “xml escaping data” or “escaping xml data” should show results related to escaping in xml. Here the term “xml” holds the weight and it controls the context, while “escaping” is a term with a relationship to “xml” and means escaping special characters within the “xml” context. “data” is again related to the context “xml” and means all data related to “xml escaping”. “xml data” will have a lesser weight and could mean pages with “xml” and any data. Another example would be “tantrums trouble discipline 10 year old”. Here the context is a 10 year old, and trouble, discipline, tantrums are in relationship to the 10 year old. This could be rephrased as 10 year old, need a solution for tantrums, trouble, and discipline. The idea here is that users usually enter search terms as keywords and not Natural Language, so contexts could be made out from the keywords. The search terms could be ended with a phrase such as, “what to do”, for eg. “toothache relief, what to do”, will show results related to finding a relief for a tooth ache. A person looking for information about taxes needs to be given information related to taxes being filed, and maybe from the IRS and the state tax website during tax season. A person looking to solve a problem in Java needs to have information retrieved from the sun.com websites or a person looking to solve a problem in the open source tomcat application server needs to have information searched on the open source related websites and the many discussion groups related to the open source. This would provide more meaningful information. 
   The third index is an unknown term index and gets built using the intelligence of the users to associate a meaning to a search term and the results. The idea here is to use the user&#39;s knowledge of the returned results to semantically associate the search terms and the results. A search term starts as an unknown term the first time it is entered by the user i.e., the search term does not exist in the database. The search term is sent to the search engines and the returned results are stored with the search term as the index key. If the user selects any of the results, the selection is given a weight. If another user searches for similar terms, the stored results are returned using the weight to rank the results. Results with more weight will be at the top of the returned results. So as users keep searching and keep selecting the results the index keeps getting built with weights tracking the results. So when users search for a term “xml escaping data”, the most selected pages are shown first. This allows popular pages to be at the top of the search. These pages will be related to the problem of “escaping XML”, “XML data”, “XML data tools”, etc. The next step in this process is to collect all the search terms with the weights and see if they can link together, so that searching for “XML data” will also show terms with “escaping XML” and “XML schema”. 
     FIG. 1  is an illustration of a user searching for the term “xml escaping”. The results from the search are shown.  101  is the search term entered by the user.  102  is the results returned from the known index.  103  is the results from the unknown index. There is no guiding info associated with the known index.  104  is the results which the user is interested in viewing. But they are ranked  4  and  5 . Let us assume the user selects  4  to read its contents, and then selects  5 . 
   When the user selects  4 , the weight attribute of the SearchDocument is incremented by 0.01. SearchDocument is an object that is stored in an ArrayList. It holds the contents in a Document Object and also has other attributes. weight is one of the attributes. Document is an org.apache.lucene.document.Document object. It has the following fields, “all” field, which holds the search term entered by the user, “id” field which holds the id for the object, “context” field holds a phrase showing the context of the url page, “url” field holds the url to access the pages. An org.apache.lucene.index.IndexWriter object is used to create and manage an index of results returned from the search engines. A Document object is created for each url returned by the search engines, and the Document object is added to the index. A SearchDocument object is created for each Document object and the contents of the Document are copied. The same process is followed when the user selects  5 . 
     FIG. 2  is an illustration of a second user searching for “xml escaping”, something similar to what the first user was searching.  201  is the search term entered by the user.  202  is the results returned by the known index.  203  is the search returned by the unknown index.  204  is the new ranking for item number  4  selected by user 1 .  205  is the new ranking for item number  5  selected by user 1 . The weights for  4  and  5  were increased by 0.01 when user 1  selected the links. There is no guiding info associated for the search. 
   Let us assume that user 2  now selects the 2nd ranking now (previously number  5 ) and then selects link at ranking  3 . The weight for  2  goes up by 0.01 and so the new weight will be 1.02. The weight for  3  goes up by 0.01 so will increase to 1.01. The weights are incremented by changing the weight attribute value in the SearchObjects for  2  and  3 . If a new user, user 3  searches for “xml escaping”, the following results will be returned, the item at ranking  2  previously, is now at  1 , item at ranking  1  previously is at ranking  2 , and item  3  at ranking  3  previously, is at  3 .  FIG. 3  is an illustration of the search results returned for user 3 .  301  is the search term,  302  is the results from the known index, and  303  is the results from the unknown index.  304 ,  305 ,  306  show the new rankings.  304  is now ranked  1 , was ranked  2  for user 2 .  305  is now ranked  2 , was ranked  1  for user 2 .  306  is at rank 3  for user 2  and user 3 . 
     FIGS. 4   a ,  4   b  are flow charts illustrating the index being created, links being added dynamically, and a walk through the search process.  401  is the step where a user enters the search terms. This could be in a browser window search field in an embodiment.  402  is the step where the search terms are sent to other search engines and also looked up in the known, semantic and unknown index.  403 , the search term is also looked up in the known index for a match. If there is a match, the matched results are added to the returned results.  404 , the results are looked up using the IndexSearcher object. Next a query object is created using the Query.parse method with the search term as one of the parameters. The other parameters are the field name, and the type of Analyzer. The analyzer used is a PorterStemAnalyzer. A BoostHitCollector is used to collect the results from the query. The searcher object is used to query for the search results. The Query object and the BoostHitCollector object are the parameters for the search. The results of the query are obtained by calling the BoostHitCollector getResults method. This returns a binary tree, TreeSet object. The tree has to be traversed to obtain the results. During the search process, the BoostHitCollector collect method is called with doc index and the score for the item. The collect method checks to see if the score is greater than 0.75 so as to include the results; the doe index is used to obtain the Document object, and the id field is retrieved. This id field is then used to index into the ArrayList of SearchObjects. The SearchObject is then added to another ArrayList which is then added to a TreeSet object to hold the results. 
     405 , the SearchObject has an attribute, guidinginfo, and if this is true, the category_id attribute holds the category for the guiding info, and the sub_category_id attribute holds the sub_category_info.  406 , if the guidinginfo attribute is true, the guiding info is also shown with the search results. 
     407 ,  408 ,  409 ,  410  look up information from the semantic index to find matches with a semantic relationship. The semantic index match might involve transforming the search terms with additions or deletions or rewording to enable a semantic search. For eg., the search terms “xml escaping” could be transformed to “xml and (escaping or encoding decoding or &amp;amp;)” ie. terms associated with xml escaping, to query the index and the other search engines. A SemanticAnalyzer is used to transform the search terms entered by the user. The search terms are parameters to the contextTerms methods which returns the transformed terms that can be sent to search engines or looked up in the indexes. 
     FIG. 4   b ,  411  shows the search terms being looked up in the unknown index. The unknown index again uses the IndexSearcher and a PorterStemAnalyzer. Next a query object is created using the Query.parse method with the search term as one of the parameters. The other parameters are the field name, and the type of Analyzer. A BoostHitCollector is again used to collect the results from the query. The searcher object is used to query for the search results. The Query object and the BoostHitCollector object are the parameters for the search.  412 , the results of the query are obtained by calling the BoostHitCollector get results method. This returns a binary tree, TreeSet object. The tree has to be traversed to obtain the results. During the search process, the BoostHitCollector collect method is called with doc index and the score for the item. The collect method checks to see if the score is greater than 0.75 so as to include the results; the doc index is used to obtain the Document object, and the id field is retrieved. This id field is then used to index into the ArrayList of SearchObjects. The SearchObject is then added to another ArrayList which is then added to a TreeSet object to hold the results. The TreeSet object uses the comparator method to sort the results. The comparator object, BoostComparator is supplied to the TreeSet object at creation. The comparator method has two parameters, which are both ArrayLists. The first object in the two ArrayLists are retrieved, and this should be the SearchObject added to the ArrayList in the BoostHitCollector collect method. The weight attribute in the SearchObjects are compared and a 0, 1 or −1 is returned. The weight attribute is set based on the user selecting a link. 
     413 , Results returned by the other search engines are mixed with the results from the known, semantic and unknown indexes and the mixed results are again ranked by relevance taking into account any user feedback.  FIG. 8  shows search results from the query “xml escaping”.  801  shows results from the known index and also shows the terms used in the search. Results shown are from a search within the known index without any mix from other search engines.  802  shows results from the semantic index. The results from the index have been mixed with results from an external search engine in this embodiment.  802  also shows the modified terms used in the search, “xml and (escaping or encoding or decoding)”. The modified terms are used to search within the semantic index and also sent out to an external search engine. The returned results are mixed and ranked based on relevance and user feedback.  803  shows a link ranked at position  4  in the shown results that is very pertinent to the search terms “xml escaping”. This link will be selected to access the link contents.  804  shows results from the query “xml escaping” in the unknown index and also the terms used for the search. The results shown have been mixed with results from an external search engine in this embodiment.  804  also shows that the terms used for the search within the index and that is sent out to the external engine.  805  shows results with feedback from the unknown index mixed with results from the external search engine. The results from the unknown index have weights 1.01 as shown in this embodiment and the mixed results have been ranked based on relevance and user feedback. 
     FIG. 9  shows results for the same query “xml escaping” made by another user.  901  shows results from the known index and also shows the terms used in the search.  901  is similar to  801  as there have been no changes to the index.  901  will be the link selected by the user in this embodiment.  902  shows results from the semantic index.  902  also shows the terms “xml and (escaping or encoding or decoding)” used in the search within the index and sent out to an external search engine.  902  also shows the new ranking of the results taking into account the feedback from  803  where a link was selected by the user. The link is now ranked at the  1   st  position.  903  shows results from the unknown index. It also shows the terms used to search within the index and that is sent out to an external search engine. The results and ranking are the same as seen in  804  and  805  as there have been no changes to the index. 
     FIG. 10  shows results for the same query “xml escaping”, made by another user.  1001  shows results from the known index and also shows the terms used in the search.  1001  is similar to  901  and  801  but now shows the feedback from the link selection in  901 . The weight for the link is now  1 . 01 .  1002  shows results from the semantic index and also shows the terms “xml and (escaping or encoding or decoding)” used in the search within the index and that is sent out to the external search engine.  1002  is similar to  902  as there is no change in the ranking.  1003  shows results from the unknown index. It also shows the terms used in the search and that is sent out to the search engine. The results and ranking are the same as seen in  903 ,  804  and  805  as there have been no changes to the index. 
   Adding to an index based on feedback in an embodiment is by creating a new Document object. The search terms are added to the “all” field as the value to be indexed. A Text field is used for indexing. Next a new ID is requested from the ID generator, and this is added to an “id” field. The “id” field is again a Text field. The URL from the search engine is added to the “url” field. A Keyword field is used to hold this. The context text with the URL is stored in a “context” field. The Document object is then added to the index using the IndexWriter. Before the document object is added, a check is made so that duplicate URLs arc not stored. The feedback weight is stored in a database using the id as the key along with terms, index, ip address, etc. When users click on a link, the link id is used to access the database and add the link if it does not exist or update the weight by incrementing it by 0.01 before transferring the user in an embodiment. When the index is accessed next, the weight is accessed using the id from the database to rank the results based on feedback and relevance. 
     414 , the results are returned to be rendered on to the user. 
   It should be noted that in the described embodiment, an object oriented programming environment has been described to discuss the present invention. Object Oriented Constructs such as methods, object, and exceptions have been used to describe how the invention works. However, this can also be implemented in other programming environments, languages and with different library components. It should also be noted that this is an illustration of one embodiment, and the search and insertion into the indexes can be done in parallel, the insertion might also be done only if needed as by feedback or every time to build the indexes. In this embodiment the external search engine results are shown to mix with semantic and unknown indexes but can be mixed with the known index results also. The search is illustrated making use of an external search engine and mixing the results but in another embodiment, only the intelligent index can be searched without the need for other search engines either internal or external. The feedback is stored in the database in this embodiment but in another embodiment the link in the index itself can be modified to keep track of the feedback. Feedback as illustrated in the embodiment increments a weight but in another embodiment feedback can decrement the weight so as to lower a link&#39;s relevance. In this embodiment all three indexes have been rendered but in other embodiments just one of the index like the unknown index can be rendered or all the indexes combined and rendered under a single results section. The descriptions shown are illustration of an embodiment and can be implemented in a plurality of ways and should not be limited to as described. 
     FIG. 5  is a flowchart illustrating what happens when a user selects a link by clicking on it.  501 , the user clicks on a link.  502 , the control is forwarded to InternetForwardController object.  503 , the ID is retrieved using the getParameter method.  504 , the forward url is retrieved using the getParameter.  505 , the search object is retrieved from the HttpSession, and the incWeight method is called using the id as the parameter.  506 , the incWeight method indexes into the ArrayList of SearchObjects and retrieves the SearchObject for the id. The weight attribute is incremented by 0.01.  507 , the forward url is decoded using URLDecoder and the control is passed to the url page using the sendRedirect method. 
     FIG. 6  is an illustration of the search results shown along with guiding info.  601  is the search terms entered by the user, “Panasonic TV”.  602  is the results returned from the known index. The control is transferred to the InternetSearchController object, when the user hits enter key or search button The InternetControllerObject creates a Search object if one has not been created. A search object reads the documents in the existing indexes on disk and initializes the ArrayList. The InternetSearchController then makes the search needed to search Google and Yahoo search engines. The search request is sent to Google and Yahoo search engines through searchGoogle and searchYahoo methods. The results returned from google are added to the UnknownIndex through indexGoogle method. This checks if the results already exist in the index, if not they are added to the index. The results from the Knownindex are obtained through getKnownIndexResults method. The getKnownIndexResults method returns a SearchDocument object if guidinginfo needs to be shown. The SearchDocument object contains the category_id and sub_category_id of the guiding info to be shown.  603  is the results from the Unknownindex.  604  is the guiding info for “Panasonic TV” the search term entered by the user. The guidinginfo is like an expert engine, the next generation comparative shopping portal, and provides information on the selected category and sub category along with a guide selection of popular choices, the lowest and average prices for the popular choices locally as well as nationally.  605  is information about the sub category “Panasonic TV”.  606  is the popular guide selections.  FIG. 7  is an illustration of the bottom part of the browser from  FIG. 6. 701  is the results returned from Google search engine.  702  is the results returned from Yahoo search engine.  703  is the lowest and average prices for the popular guide selection. The selection shows all the popular “Panasonic TV” models. 
   It should be noted that in the described embodiment search engines google and yahoo have been shown. This is an illustration of one embodiment and other search engines including its own search engines can be used to build the intelligent index. 
   
     
       
             
           
             
             
           
         
             
                 
             
             
               Definition List 1 
             
           
        
         
             
               Term 
               Definition 
             
             
                 
             
             
               GET 
               A HTTP method to send/get data from a 
             
             
                 
               server 
             
             
               POST 
               A HTTP method to send/get data from a 
             
             
                 
               server 
             
             
               HTTP 
               A TCP/IP based text request/response 
             
             
                 
               protocol that allows web browsers to show 
             
             
                 
               data from a server called a web server 
             
             
               Session 
               A J2EE HTTP session object, and provides a 
             
             
                 
               way to identify a user across more than one 
             
             
                 
               page request or visit to a Web site and to 
             
             
                 
               store information about that user. 
             
             
               J2EE 
               Java 2 Platform, Enterprise Edition defines 
             
             
                 
               the standard for developing component- 
             
             
                 
               based multitier enterprise applications 
             
             
               Jsp 
               JavaServer Pages technology provides a 
             
             
                 
               simplified, fast way to create dynamic web 
             
             
                 
               content 
             
             
               lucene 
               An open source search API 
             
             
               Salmon Open Source 
               An open source MVC J2EE server framework 
             
             
               Framework 
               for building J2EE applications 
             
             
               URL 
               Uniform Resource Locator 
             
             
               XML 
               Extensible Markup Language 
             
             
               TCP 
               Transmission Control Protocol 
             
             
               IP 
               Internet Protocol 
             
             
               Java 
               An object oriented programming language 
             
             
               MVC 
               Model View Controller paradigm 
             
             
               Web 
               World wide web, www, also known as the 
             
             
                 
               internet 
             
             
               Browser 
               A Graphical User Interface for viewing 
             
             
                 
               content called html on the web 
             
             
               ENeedsOnline 
               An online marketplace where buyers and 
             
             
                 
               sellers can make available a need, 
             
             
                 
               http://www.eneedsonline.com 
             
             
               BoostHitCollector 
               A Java Object that is used to collect search 
             
             
                 
               the results 
             
             
               Commscore 
               Market research firm 
             
             
               Google 
               http://www.google.com 
             
             
               Yahoo 
               http://www.yahoo.com 
             
             
                 
             
           
        
       
     
   
   While embodiments and applications of this invention have been shown and described, it would be apparent to those skilled in the art having benefit of this disclosure that many more modifications than mentioned above are possible without departing from the inventive concepts herein. The invention, therefore, is not to be restricted except in the spirit of the appended claims.