Search for Minority Information from Wikipedia Based on Similarity of Majority Information

  • Yuki Hattori
  • Akiyo Nadamoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7235)


In this research, we propose a method of searching for minority information, which is less acknowledged and less popular, on the internet. We propose two methods to extract minority information. One is that of calculating relevance of content. The other is based on analogy expression. In this paper, we propose such a minority search system. At this time, we consider it necessary to search for minority information in which a user is interested. Using our proposed system, the user inputs a query which represents their interest in majority information. Then the system searches for minority information that is similar to the majority information provided. Consequently, users can obtain the new information that users do not know and can discover new knowledge and new interests.


User Input Basic Node Analogy Search Anchor Text Link Graph 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ohshima, H., Oyama, S., Tanaka, K.: Sibling Page Search by Page Examples. In: Sugimoto, S., Hunter, J., Rauber, A., Morishima, A. (eds.) ICADL 2006. LNCS, vol. 4312, pp. 91–100. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proc. of the 7th International Conference on World Wide Web (WWW 1998), pp. 107–117 (1998)Google Scholar
  3. 3.
    Wang, Y., Kitsuregawa, M.: Evaluation Contents-Link Coupled Web Page Clustering for Web Search Results. In: Proceedings of the 7th International Conference on Information and Knowledge Management (2002)Google Scholar
  4. 4.
    Glover, E.J., Tsioutsiouliklis, K., et al.: Flake.: Using Web structure for classifying and describing Web pages. In: Proc. of WWW12 (2002)Google Scholar
  5. 5.
    Milne, D.: Computing Semantic Relatedness using Wikipedia Link Structure. In: Proceedings of the New Zealand Computer Science Research Student Conference, NZCSRSC 2007 (2007)Google Scholar
  6. 6.
    Chernov, S., Iofciu, T., Nejdl, W., Zhuo, X.: Extracting semantic relationships between wikipedia categories. In: 1st International Workshop: SemWiki 2006 - From Wiki to Semantics (SemWiki 2006), co-located with the ESWC 2006, Budva, Montenegro, June 12 (2006)Google Scholar
  7. 7.
    Zhuang, Z., Cucerzan, S.: Re-ranking search results using query logs. In: Proceedings of the 15th International Conference on Information and Knowledge Management (CIKM 2006), Arlington, Virginia, pp. 860–861 (2006)Google Scholar
  8. 8.
    Lee, K.S., Park, Y.C., Choi, K.S.: Re-ranking model based on document clusters. Information Processing and Management 37(1), 1–14 (2001)zbMATHCrossRefGoogle Scholar
  9. 9.
    Chidlovskii, B., Glance, N.S., Grasso, M.A.: Collaborative ReRanking of Search Results. In: AAAI-2000 Workshop on AI for Web Search (2000)Google Scholar
  10. 10.
    Nakayama, K., Hara, T., Nishio, S.: Wikipedia Mining - Wikipedia as a Corpus for Knowledge Extraction. In: Proceedings of Annual Wikipedia Conference, Wikimania (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yuki Hattori
    • 1
  • Akiyo Nadamoto
    • 1
  1. 1.Konan UniversityKobeJapan

Personalised recommendations