Learning to Discover Domain-Specific Web Content

Abstract

The ability to discover all content relevant to an information domain has many applications, from helping in the understanding of humanitarian crises to countering human and arms trafficking. In such applications, time is of essence: it is crucial to both maximize coverage and identify new content as soon as it becomes available, so that appropriate actions can be taken. In this paper, we propose new methods for efficient domain-specific re-crawling that maximize the yield for new content. By learning patterns of pages that have a high yield, our methods select a small set of pages that can be re-crawled frequently, increasing the coverage and freshness while conserving resources. Unlike previous approaches to this problem, our methods combine different factors to optimize the re-crawling strategy, do not require full snapshots for the learning step, and dynamically adapt the strategy as the crawl progresses. In an empirical evaluation, we have simulated the framework over 600 partial crawl snapshots in three different domains. The results show that our approach can achieve 150% higher coverage compared to existing, state-of-the-art techniques. In addition, it is also able to capture 80% of new relevant content within less than 4 hours of publication.