Enhancements in query evaluation and page summarization of The Thinking Algorithm

This paper explores a unique way in which the thinking algorithm adds an extra logical substrate to a Web search query using artificial intelligence. Instead of just going after keyword searching, the algorithm tries to assess the motives of the user behind entering a query. The algorithm tries to find the reasons as to why a user has entered a particular query by adding this question with every query: ldquoSitting in a particular region, why has the person entered such a query?rdquo The compounded uniqueness level applies the concept of geo-location searches. The algorithm allots competency level to the user from the query term using topic trees. The query parsers and indexers in the algorithm are more magnetized into extracting meaningful information from queries and web pages than just indexing the words present in a web page. Clustering the search results help to resolve the ambiguity in a userpsilas query.

[1]  Regina Barzilay,et al.  Using Lexical Chains for Text Summarization , 1997 .

[2]  Mark Sanderson,et al.  Word sense disambiguation and information retrieval , 1994, SIGIR '94.

[3]  Chris Buckley,et al.  Automatic Text Summarization by Paragraph Extraction , 1997 .

[4]  Robert B. Allen,et al.  An interface for navigating clustered document sets returned by queries , 1993, COCS '93.

[5]  Geoffrey Zweig,et al.  Syntactic Clustering of the Web , 1997, Comput. Networks.

[6]  Wei-Ying Ma,et al.  Probabilistic model for contextual retrieval , 2004, SIGIR '04.

[7]  Daniel E. Rose,et al.  Understanding user goals in web search , 2004, WWW '04.

[8]  In-Ho Kang,et al.  Query type classification for web document retrieval , 2003, SIGIR.

[9]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[10]  Robert Krovetz,et al.  Word sense disambiguation for large text databases , 1996 .

[11]  Djoerd Hiemstra,et al.  Retrieving Web Pages Using Content, Links, URLs and Anchors , 2001, TREC.

[12]  Jade Goldstein-Stewart,et al.  Summarizing text documents: sentence selection and evaluation metrics , 1999, SIGIR '99.

[13]  Oren Etzioni,et al.  Grouper: A Dynamic Clustering Interface to Web Search Results , 1999, Comput. Networks.

[14]  Marcia J. Bates,et al.  Information search tactics , 1979, J. Am. Soc. Inf. Sci..

[15]  Jussi Karlgren,et al.  Verbosity and Interface Design , 2000 .

[16]  Stephen E. Robertson,et al.  Effective site finding using link anchor information , 2001, SIGIR '01.

[17]  Zhenyu Liu,et al.  Automatic identification of user goals in Web search , 2005, WWW '05.

[18]  Nicholas J. Belkin,et al.  Ask for Information Retrieval: Part II. Results of a Design Study , 1982, J. Documentation.

[19]  Marcia J. Bates,et al.  The design of browsing and berrypicking techniques for the online search interface , 1989 .

[20]  Hans Peter Luhn,et al.  The Automatic Creation of Literature Abstracts , 1958, IBM J. Res. Dev..

[21]  David R. Karger,et al.  Scatter/Gather: a cluster-based approach to browsing large document collections , 1992, SIGIR '92.

[22]  Monika Henzinger,et al.  Analysis of a very large web search engine query log , 1999, SIGF.

[23]  David Hawking,et al.  Overview of the TREC-2001 Web track , 2002 .

[24]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.