Focused crawler

A focused crawler is a web crawler that collects Web pages that satisfy some specific property, by carefully prioritizing the crawl frontier and managing the hyperlink exploration process. Some predicates may be based on simple, deterministic and surface properties. For example, a crawler's mission may be to crawl pages from only the .jp domain. Other predicates may be softer or comparative, e.g., "crawl pages with large PageRank", or "crawl pages about baseball". An important page property pertains to topics, leading to topical crawlers. For example, a topical crawler may be deployed to collect pages about solar power, or swine flu, while minimizing resources spent fetching pages on other topics. Crawl frontier management may not be the only device used by focused crawlers; they may use a Web directory, a Web text index, backlinks, or any other Web artifact.

A focused crawler must predict the probability that an unvisited page will be relevant before actually downloading the page. A possible predictor is the anchor text of links; this was the approach taken by Pinkerton in a crawler developed in the early days of the Web. Topical crawling was first introduced by Filippo Menczer Chakrabarti et al. coined the term focused crawler and used a text classifier to prioritize the crawl frontier. Andrew McCallum and co-authors also used reinforcement learning to focus crawlers. Diligenti 'et al. traced the context graph leading up to relevant pages, and their text content, to train classifiers. A form of online reinforcement learning has been used along with features extracted from the DOM tree and text of linking pages, to continually train classifiers that guide the crawl. In a review of topical crawling algorithms, Menczer et al. show that such simple strategies are very effective for short crawls, while more sophisticated techniques such as reinforcement learning and evolutionary adaptation can give the best performance over longer crawls. It has been shown that spatial information is important to classify Web documents.

Another type of focused crawlers is semantic focused crawler, which makes use of domain ontologies to represent topical maps and link Web pages with relevant ontological concepts for the selection and categorization purposes. In addition, ontologies can be automatically updated in the crawling process. Dong et al. introduced such an ontology-learning-based crawler using support vector machine to update the content of ontological concepts when crawling Web Pages.

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