Focused and Adaptive Crawling for Topic Specific and Hidden Web Entries

In this paper we describe new adaptive crawling strategies to efficiently locate the entry points to hidden-Web sources and we describe a new hypertext resource discovery system called a Focused Crawler. The fact that hidden-Web sources are very sparsely distributed makes the problem of locating them especially challenging. We deal with this problem by using the contents of pages to focus the crawl on a topic, by prioritizing promising links within the topic, and by also following links that may not lead to immediate benefit. We propose a new framework whereby crawlers automatically learn patterns of promising links and adapt their focus as the crawl progresses, thus greatly reducing the amount of required manual setup and tuning. The goal of a focused crawler is to selectively seek out pages that are relevant to a pre-defined set of topics. The topics are specified not using s, but using exemplary documents. Rather than collecting and indexing all accessible Web documents to be able to answer all possible ad-hoc queries, a focused crawler analyzes its crawl boundary to find the links that are likely to be most relevant for the crawl, and avoid and network resources, and helps keep the crawl more up-to-dates we designed two hypertext mining programs that guide our crawler a classifier that evaluates the relevance of a hypertext document with respect to the focus topics, and a distiller that identifies hypertext nodes that are great access points to many relevant pages within a few links, Irrelevant regions of the Web. This leads to significant savings in hardware. Our experiments over real Web pages in a representative set of domains indicate that online learning leads to significant gains in harvest ratesthe adaptive crawlers retrieve up to three times as many forms as crawlers that use a fixed focus strategy.