Classifying Web Pages by Using Knowledge Bases for Entity Retrieval

  • Yusuke Kiritani
  • Qiang Ma
  • Masatoshi Yoshikawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5690)


In this paper, we propose a novel method to classify Web pages by using knowledge bases for entity search, which is a kind of typical Web search for information related to a person, location or organization. First, we map a Web page to entities according to the similarities between the page and the entities. Various methods for computing such similarity are applied. For example, we can compute the similarity between a given page and a Wikipedia article describing a certain entity. The frequency of an entity appearing in the page is another factor used in computing the similarity. Second, we construct a directed acyclic graph, named PEC graph, based on the relations among Web pages, entities, and categories, by referring to YAGO, a knowledge base built on Wikipedia and WordNet. Finally, by analyzing the PEC graph, we classify Web pages into categories. The results of some preliminary experiments validate the methods proposed in this paper.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yusuke Kiritani
    • 1
  • Qiang Ma
    • 1
  • Masatoshi Yoshikawa
    • 1
  1. 1.Department of Social Informatics, Graduate School of InformaticesKyoto UniversityKyotoJapan

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