National Journal of System and Information Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Creating Web Unite of Web Communities and Derive Astonishing Information from Web Unite


     

   Subscribe/Renew Journal


The Web harbors a large number of communities - groups of content-creators sharing a common interest - each of which manifests it self as a set of interlinked Web pages. New groups and commercial Web directories together contain of the order of 20,000 such communities; our particular interest here is on particular topic based communities. There is a type of information called Unexpected Information, which is of great interest. Finding unexpected information is useful in many applications. For example, it is useful for a company to find unexpected information about its competitors, e.g., unexpected services and products that its competitors offer. With this information, the company can learn from its competitors and/or design counter measures to improve its competitiveness. The research tries to form a group of common objective web sites and then derive information by comparing those web sites. The research proposes a methodology through which we can group all those same type of web sites and can find out some unexpected information from it.

Keywords

Web Unite, Web Mining, Information Extraction
Subscription Login to verify subscription
User
Notifications
Font Size


  • R. Agrawal and R. Srikanth, Fast algorithms for mining association rules, in: Proc. VLDB, Santiago, Chile, September 1994.

  • K. Bharat and A. Broder, A technique for measuring the relative size and overlap of public Web search engines, in: Proc. 7th Int. World Wide Web Conference, Brisbane, Australia, Elsevier, Amsterdam, April 1998, pp. 379–388.

  • S. Brin and L. Page, The anatomy of a large scale hypertextual web search engine, in: Proc. 7th Int. World Wide Web Conference, Brisbane, Australia, April 1998, pp. 107–117.

  • A. Broder, S. Glassman, M. Manasse and G. Zweig, Syntactic clustering of the Web, in: Proc. 6th Int. World Wide Web Conference, April 1997, pp. 391–404.

  • S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg, P. Raghavan and S. Rajagopalan, Automatic resource compilation by analyzing hyperlink structure and associated text, in: Proc. 7th World-Wide Web Conference, 1998, pp. 65–74.

  • D. Florescu, A. Levy, A. Mendelzon, Database techniques for the World-Wide Web: a survey, SIGMOD Rec. 27(3) (1998) 59–74.

  • D. Gibson, J. Kleinberg, P. Raghavan, Inferring Web communities from link topology, in: Proc. 9th ACM Conf. On Hypertext and Hypermedia, 1998.

  • L.V.S. Lakshmanan, F. Sadri and I.N. Subramanian, A declarative approach to querying and restructuring the World-Wide-Web, in: Post-ICDE Workshop on Research Issues in Data Engineering (RIDE’96), New Orleans, February 1996.

  • P. Pirolli, J. Pitkow and R. Rao, Silk from a sow’s ear: extracting usable structures from the Web, in: Proc. ACM SIGCHI Conf. on Human Factors in Computing, 1996.

  • E. Spertus, ParaSite: mining structural information on the Web, in: Proc. 6th Int. World Wide Web Conference, 1997.

  • R. Agrawal and R. Srikant. Fast algorithms for mining association rules. VLDB-94, 1994.

  • S. Brin, and L. Page. The anatomy of a large scale hypertextual Web search engine. WWW7, 1998.

  • S. Chakrabarti, M. van den Berg, and B. Dom. Focused crawling: a new approach to topic-specific Web resource discovery. WWW8, 1999.

  • M. Craven, D. DiPasquo. D. Freitag, A. McCallum, T. Mitchell, K. Nigam and S. Slattery. Learning to construct knowledge bases from the World Wide Web. Artificial Intelligence, 118(1-2), 2000.

  • J. Dean and M. R. Henzinger. Finding related pages in the World Wide Web. WWW8, 1999.

  • D. Gibson, J. Kleinberg, P. Raghavan. Inferring web communities from link topology. Proc. 9th ACM Conference on Hypertext and Hypermedia, 1998.

  • T. Guan and K. F. Wong. KPS - a Web information mining algorithm. WWW8, 1999.

  • Resource Description Framework (RDF) Schema Specification, W3C proposed recommendation. 22 Feb, 1999. http://www.w3.org/TR/PR-rdf-schema/


Abstract Views: 336

PDF Views: 2




  • Creating Web Unite of Web Communities and Derive Astonishing Information from Web Unite

Abstract Views: 336  |  PDF Views: 2

Authors

Abstract


The Web harbors a large number of communities - groups of content-creators sharing a common interest - each of which manifests it self as a set of interlinked Web pages. New groups and commercial Web directories together contain of the order of 20,000 such communities; our particular interest here is on particular topic based communities. There is a type of information called Unexpected Information, which is of great interest. Finding unexpected information is useful in many applications. For example, it is useful for a company to find unexpected information about its competitors, e.g., unexpected services and products that its competitors offer. With this information, the company can learn from its competitors and/or design counter measures to improve its competitiveness. The research tries to form a group of common objective web sites and then derive information by comparing those web sites. The research proposes a methodology through which we can group all those same type of web sites and can find out some unexpected information from it.

Keywords


Web Unite, Web Mining, Information Extraction

References