document: An adaptive crawler for locating hidden-Web entry points 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 .
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