fine-grain web site structure discovery
Several techniques have been recently proposed to automatically derive web wrappers, i.e., programs that extract data from HTML pages, and transform them into a more structured format, typically in XML syntax. These techniques automatically induce a wrapper from a set of sample pages that share a common HTML template. An open issue, however, is how to collect suitable classes of sample pages to feed the wrapper inducer. Presently, the pages are chosen manually.In this paper, we tackle the problem of automatically discovering the main classes of pages offered by a site by exploring only a small, representative, portion of it. The web site model we propose describes the structure of the site as a graph whose nodes are classes of pages that share a common structure, and whose edges represent links among instances of the page classes. Using this model, we have developed an algorithm that accepts the url of an entry point to the target web site, visits a limited portion of the site, and produces an accurate model of the site structure. We also report on preliminary experiments performed on actual web sites, that have produced encouraging results.
web modeling information extraction web information systems wrapper induction clustering