Focused crawler

{{Short description|Type of web 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.Soumen Chakrabarti, [http://www.springerreference.com/docs/html/chapterdbid/63300.html Focused Web Crawling], in the [http://www.springerreference.com/docs/navigation.do?m=Encyclopedia+of+Database+Systems+(Computer+Science)-book65 Encyclopedia of Database Systems]. 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 about baseball", or "crawl pages with large PageRank". 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, swine flu, or even more abstract concepts like controversy[https://www.semanticjuice.com/controversial-topics/ Controversial topics] 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.[http://www.intelligence.tuc.gr/~petrakis/publications/BaPeMi09.pdf Improving the Performance of Focused Web Crawlers][https://qoqlinks.com], Sotiris Batsakis, Euripides G. M. Petrakis, Evangelos Milios, 2012-04-09 A possible predictor is the anchor text of links; this was the approach taken by PinkertonPinkerton, B. (1994). [http://www.thinkpink.com/bp/WebCrawler/WWW94.html Finding what people want: Experiences with the WebCrawler]. In Proceedings of the First World Wide Web Conference, Geneva, Switzerland. in a crawler developed in the early days of the Web. Topical crawling was first introduced by Filippo Menczer.Menczer, F. (1997). [http://informatics.indiana.edu/fil/Papers/ICML.ps ARACHNID: Adaptive Retrieval Agents Choosing Heuristic Neighborhoods for Information Discovery] {{Webarchive|url=https://web.archive.org/web/20121221113620/http://informatics.indiana.edu/fil/Papers/ICML.ps |date=2012-12-21 }}. In D. Fisher, ed., Proceedings of the 14th International Conference on Machine Learning (ICML97). Morgan Kaufmann.Menczer, F. and Belew, R.K. (1998). [http://informatics.indiana.edu/fil/Papers/AA98.ps Adaptive Information Agents in Distributed Textual Environments] {{Webarchive|url=https://web.archive.org/web/20121221113630/http://informatics.indiana.edu/fil/Papers/AA98.ps |date=2012-12-21 }}. In K. Sycara and M. Wooldridge (eds.) Proceedings of the 2nd International Conference on Autonomous Agents (Agents '98). ACM Press. Chakrabarti et al. coined the term 'focused crawler' and used a text classifier[http://www8.org/w8-papers/5a-search-query/crawling/index.html Focused crawling: a new approach to topic-specific Web resource discovery], Soumen Chakrabarti, Martin van den Berg and Byron Dom, WWW 1999. to prioritize the crawl frontier. Andrew McCallum and co-authors also used reinforcement learning[http://dl.acm.org/citation.cfm?id=1624313 A machine learning approach to building domain-specific search engines], Andrew McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore, IJCAI 1999.[http://dl.acm.org/citation.cfm?id=657633 Using Reinforcement Learning to Spider the Web Efficiently], Jason Rennie and Andrew McCallum, ICML 1999. to focus crawlers. Diligenti et al. traced the context graphDiligenti, M., Coetzee, F., Lawrence, S., Giles, C. L., and Gori, M. (2000). [http://nautilus.dii.unisi.it/pubblicazioni/files/conference/2000-Diligenti-VLDB.pdf Focused crawling using context graphs] {{Webarchive|url=https://web.archive.org/web/20080307065612/http://nautilus.dii.unisi.it/pubblicazioni/files/conference/2000-Diligenti-VLDB.pdf |date=2008-03-07 }}. In Proceedings of the 26th International Conference on Very Large Databases (VLDB), pages 527-534, Cairo, Egypt. 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[http://dl.acm.org/citation.cfm?id=511466 Accelerated focused crawling through online relevance feedback], Soumen Chakrabarti, Kunal Punera, and Mallela Subramanyam, WWW 2002. classifiers that guide the crawl. In a review of topical crawling algorithms, Menczer et al.Menczer, F., Pant, G., and Srinivasan, P. (2004). [http://doi.acm.org/10.1145/1031114.1031117 Topical Web Crawlers: Evaluating Adaptive Algorithms]. ACM Trans. on Internet Technology 4(4): 378–419. 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.[https://ieeexplore.ieee.org/document/1183910/ Recognition of common areas in a Web page using visual information: a possible application in a page classification], Milos Kovacevic, Michelangelo Diligenti, Marco Gori, Veljko Milutinovic, Data Mining, 2002. ICDM 2003.

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.Dong, H., Hussain, F.K., Chang, E.: [https://www.researchgate.net/publication/44241179_State_of_the_Art_in_Semantic_Focused_Crawlers State of the art in semantic focused crawlers]. Computational Science and Its Applications – ICCSA 2009. Springer-Verlag, Seoul, Korea (July 2009) pp. 910-924 In addition, ontologies can be automatically updated in the crawling process. Dong et al.Dong, H., Hussain, F.K.: [https://www.researchgate.net/publication/264620349_SOF_A_semi-supervised_ontology-learning-based_focused_crawler SOF: A semi-supervised ontology-learning-based focused crawler.] Concurrency and Computation: Practice and Experience. 25(12) (August 2013) pp. 1623-1812 introduced such an ontology-learning-based crawler using support vector machine to update the content of ontological concepts when crawling Web Pages.

Crawlers are also focused on page properties other than topics. Cho et al.Junghoo Cho, Hector Garcia-Molina, Lawrence Page: [http://dl.acm.org/citation.cfm?id=297835 Efficient Crawling Through URL Ordering]. Computer Networks 30(1-7): 161-172 (1998) study a variety of crawl prioritization policies and their effects on the link popularity of fetched pages. Najork and WeinerMarc Najork, Janet L. Wiener: [http://dl.acm.org/citation.cfm?id=371965 Breadth-first crawling yields high-quality pages]. WWW 2001: 114-118 show that breadth-first crawling, starting from popular seed pages, leads to collecting large-PageRank pages early in the crawl. Refinements involving detection of stale (poorly maintained) pages have been reported by Eiron et al.Nadav Eiron, Kevin S. McCurley, John A. Tomlin: [http://dl.acm.org/citation.cfm?id=988714 Ranking the web frontier]. WWW 2004: 309-318.

A kind of semantic focused crawler, making use of the idea of reinforcement learning has been introduced by Meusel et al.Meusel R., Mika P., Blanco R. (2014). [http://dl.acm.org/citation.cfm?doid=2661829.2661902 Focused Crawling for Structured Data]. ACM International Conference on Information and Knowledge Management, Pages 1039-1048. using online-based classification algorithms in combination with a bandit-based selection strategy to efficiently crawl pages with markup languages like RDFa, Microformats, and Microdata.

The performance of a focused crawler depends on the richness of links in the specific topic being searched, and focused crawling usually relies on a general web search engine for providing starting points. DavisonBrian D. Davison: [http://dl.acm.org/citation.cfm?doid=345508.345597 Topical locality in the Web]. SIGIR 2000: 272-279. presented studies on Web links and text that explain why focused crawling succeeds on broad topics; similar studies were presented by Chakrabarti et al.Soumen Chakrabarti, Mukul Joshi, Kunal Punera, David M. Pennock: [http://dl.acm.org/citation.cfm?id=511480 The structure of broad topics on the Web]. WWW 2002: 251-262. Seed selection can be important for focused crawlers and significantly influence the crawling efficiency.Jian Wu, Pradeep Teregowda, Juan Pablo Fernández Ramírez, Prasenjit Mitra, Shuyi Zheng, C. Lee Giles, [http://dl.acm.org/citation.cfm?id=2380718.2380762 The evolution of a crawling strategy for an academic document search engine: whitelists and blacklists], In proceedings of the 3rd Annual ACM Web Science Conference Pages 340-343, Evanston, IL, USA, June 2012. A whitelist strategy is to start the focus crawl from a list of high quality seed URLs and limit the crawling scope to the domains of these URLs. These high quality seeds should be selected based on a list of URL candidates which are accumulated over a sufficiently long period of general web crawling. The whitelist should be updated periodically after it is created.