Focused crawling for the hidden web

A constantly growing amount of high-quality information resides in databases and is guarded behind forms that users fill out and submit. The Hidden Web comprises all these information sources that conventional web crawlers are incapable of discovering. In order to excavate and make available meaningful data from the Hidden Web, previous work has focused on developing query generation techniques that aim at downloading all the content of a given Hidden Web site with the minimum cost. However, there are circumstances where only a specific part of such a site might be of interest. For example, a politics portal should not have to waste bandwidth or processing power to retrieve sports articles just because they are residing in databases also containing documents relevant to politics. In cases like this one, we need to make the best use of our resources in downloading only the portion of the Hidden Web site that we are interested in. We investigate how we can build a focused Hidden Web crawler that can autonomously extract topic-specific pages from the Hidden Web by searching only the subset that is related to the corresponding area. In this regard, we present an approach that progresses iteratively and analyzes the returned results in order to extract terms that capture the essence of the topic we are interested in. We propose a number of different crawling policies and we experimentally evaluate them with data from four popular sites. Our approach is able to download most of the content in search in all cases, using a significantly smaller number of queries compared to existing approaches.