• DocumentCode
    3312114
  • Title

    Improving Retrieval Performance with Wikipedia´s Category Knowledge

  • Author

    Zeng, Yin ; Lin, Wu ; Lei, Kai ; Huang, Lian´en

  • Author_Institution
    Shenzhen Key Lab. for Cloud Comput. Technol. & Applic. (SPCCTA), Peking Univ., Shenzhen, China
  • fYear
    2012
  • fDate
    17-19 Aug. 2012
  • Firstpage
    449
  • Lastpage
    452
  • Abstract
    For text search systems, the ambiguity of short queries often leads to poor performance. To solve this problem, relevance feedback via query-expansion is considered as one effective technique. However, many methods of relevance feedback barely use the knowledge of search results and the improvement of effectiveness is limited because the knowledge used is limited. In this paper we try to include Wikipedia´s category knowledge to improve the poor retrieval performance. A method of category feedback is proposed, which is based on the information of Wikipedia categories. Categories instead of terms and documents are provided to users for feedback. Finally, an experimental search system is developed which demonstrates the effectiveness of our method.
  • Keywords
    Web sites; query processing; relevance feedback; text analysis; Wikipedia category knowledge; category feedback; document handling; information retrieval; query expansion; query retrieval; relevance feedback; text search systems; Database languages; Electronic publishing; Encyclopedias; Information retrieval; Internet; Standards; Wikipedia; category feedback; information retrieval; query expansion;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4673-2406-9
  • Type

    conf

  • DOI
    10.1109/ICCIS.2012.174
  • Filename
    6299999