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Templates are pieces of HTML code common to a set of web pages usually adopted by content providers to enhance the uniformity of layout and navigation of theirs Web sites. They are usually generated using authoring/publishing tools or by programs that build HTML pages to publish content from a database. In spite of their usefulness, the content of templates can negatively affect the quality of results produced by systems that automatically process information available in web sites, such as search engines, clustering and automatic categorization programs. Further, the information available in templates is redundant and thus processing and storing such information just once for a set of pages may save computational resources. In this paper, we present and evaluate methods for detecting templates considering a scenario where multiple templates can be found in a collection of Web pages. Most of previous work have studied template detection algorithms in a scenario where the collection has just a single template. The scenario with multiple templates is more realistic and, as it is discussed here, it raises important questions that may require 172 World Wide Web (2009) 12:171-211 extensions and adjustments in previously proposed template detection algorithms. We show how to apply and evaluate two template detection algorithms in this scenario, creating solutions for detecting multiple templates. The methods studied partitions the input collection into clusters that contain common HTML paths and share a high number of HTML nodes and then apply a single-template detection procedure over each cluster. We also propose a new algorithm for single template detection based on a restricted form of bottom-up tree-mapping that requires only small set of pages to correctly identify a template and which has a worst-case linear complexity. Our experimental results over a representative set of Web pages show that our approach is efficient and scalable while obtaining accurate results.

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