A pattern-based selective recrawling approach for object-level vertical search

Traditional recrawling methods learn navigation patterns in order to crawl related web pages. However, they cannot remove the redundancy found on the web, especially at the object level. To deal with this problem, we propose a new hypertext resource discovery method, called ``selective recrawling'' for object-level vertical search applications. The goal of selective recrawling is to automatically generate URL patterns, then select those pages that have the widest coverage, and least irrelevance and redundancy relative to a pre-defined vertical domain. This method only requires a few seed objects and can select the set of URL patterns that covers the greatest number of objects. The selected set can continue to be used for some time to recrawl web pages and can be renewed periodically. This leads to significant savings in hardware and network resources. In this paper we present a detailed framework of selective recrawling for object-level vertical search. The selective recrawling method automatically extends the set of candidate websites from initial seed objects. Based on the objects extracted from these websites it learns a set of URL patterns which covers the greatest number of target objects with little redundancy. Finally, the navigation patterns generated from the selected URL pattern set are used to guide future crawling. Experiments on local event data show that our method can greatly reduce downloading of web pages while maintaining comparative object coverage.