Automatically Constructing Descriptive Site Maps

  • Pavel Dmitriev
  • Carl Lagoze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3841)


Rapid increase in the number of pages on web sites, and widespread use of search engine optimization techniques, lead to web sites becoming difficult to navigate. Traditional site maps do not provide enough information about the site, and are often outdated. In this paper, we propose a machine learning based algorithm, which, combined with natural language processing, automatically constructs high quality descriptive site maps. In contrast to the previous work, our approach does not rely on heuristic rules to build site maps, and does not require specifying the number of items in a site map in advance. It also generates concise, but descriptive summaries for every site map item. Preliminary experiments with a set of educational web sites show that our method can construct site maps of high quality. An important application of our method is a new paradigm for accessing information on the Web, which integrates searching and browsing.


Search Engine Anchor Text Logical Domain Frequent Keyword Alldiff Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pavel Dmitriev
    • 1
  • Carl Lagoze
    • 1
  1. 1.Department of Computer ScienceCornell UniversityIthacaUSA

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