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The concepts of topical and focused crawling were first introduced by Filippo Menczer [20] [21] and by Soumen Chakrabarti et al. [22] The main problem in focused crawling is that in the context of a Web crawler, we would like to be able to predict the similarity of the text of a given page to the query before actually downloading the page.
Distributed web crawling is a distributed computing technique whereby Internet search engines employ many computers to index the Internet via web crawling. Such systems may allow for users to voluntarily offer their own computing and bandwidth resources towards crawling web pages.
Googlebot is the web crawler software used by Google that collects documents from the web to build a searchable index for the Google Search engine. This name is actually used to refer to two different types of web crawlers: a desktop crawler (to simulate desktop users) and a mobile crawler (to simulate a mobile user).
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In addition, ontologies can be automatically updated in the crawling process. Dong et al. [15] 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.
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Web scraping is the process of automatically mining data or collecting information from the World Wide Web. It is a field with active developments sharing a common goal with the semantic web vision, an ambitious initiative that still requires breakthroughs in text processing, semantic understanding, artificial intelligence and human-computer interactions.