An adaptive crawler for locating hiddenwebentry points

Luciano Barbosa, Juliana Freire

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we describe new adaptive crawling strategies to efficiently locate the entry points to hidden-Web sources. The fact that hidden-Web sources are very sparsely distributedmakes the problem of locating them especially challenging. We deal with this problem by using the contents ofpages to focus the crawl on a topic; by prioritizing promisinglinks within the topic; and by also following links that may not lead to immediate benefit. We propose a new frameworkwhereby crawlers automatically learn patterns of promisinglinks and adapt their focus as the crawl progresses, thus greatly reducing the amount of required manual setup andtuning. Our experiments over real Web pages in a representativeset of domains indicate that online learning leadsto significant gains in harvest rates' the adaptive crawlers retrieve up to three times as many forms as crawlers thatuse a fixed focus strategy.

Original languageEnglish (US)
Title of host publication16th International World Wide Web Conference, WWW2007
Pages441-450
Number of pages10
DOIs
StatePublished - 2007
Event16th International World Wide Web Conference, WWW2007 - Banff, AB, Canada
Duration: May 8 2007May 12 2007

Publication series

Name16th International World Wide Web Conference, WWW2007

Other

Other16th International World Wide Web Conference, WWW2007
CountryCanada
CityBanff, AB
Period5/8/075/12/07

Keywords

  • HiddenWeb
  • Learning classifiers
  • Online learning
  • Web crawling strategies

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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