Kikori-KS: An Effective and Efficient Keyword Search System for Digital Libraries in XML

  • Toshiyuki Shimizu
  • Norimasa Terada
  • Masatoshi Yoshikawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4312)


Identifying meaningful document fragments is a major advantage achieved by encoding documents in XML. In scholarly articles, such document fragments include sections, subsections and paragraphs. XML information retrieval systems need to search document fragments relevant to queries from a set of XML documents in a digital library. We present Kikori-KS, an effective and efficient XML information retrieval system for scholartic articles. Kikori-KS accepts a set of keywords as a query. This form of query is simple yet useful because users are not required to understand XML query languages or XML schema. To meet practical demands for searching relevant fragments in scholartic articles, we have developed a user-friendly interface for displaying search results. Kikori-KS was implemented on top of a relational XML database system developed by our group. By carefully designing the database schema, Kikori-KS handles a huge number of document fragments efficiently. Our experiments using INEX test collection show that Kikori-KS achieved an acceptable search time and with relatively high precision.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    W3C: XQuery 1.0 and XPath 2.0 Full-Text (2006),
  2. 2.
    W3C: XML Path Language (XPath) Version 1.0 (1999),
  3. 3.
    W3C: XQuery 1.0: An XML Query Language (2006),
  4. 4.
    Yoshikawa, M., Amagasa, T., Shimura, T., Uemura, S.: XRel: a path-based approach to storage and retrieval of XML documents using relational databases. ACM Transactions on Internet Technology 1, 110–141 (2001)CrossRefGoogle Scholar
  5. 5.
    Fujimoto, K., Shimizu, T., Terada, N., Hatano, K., Suzuki, Y., Amagasa, T., Kinutani, H., Yoshikawa, M.: Implementation of a high-speed and high-precision XML information retrieval system on relational databases. In: Fuhr, N., Lalmas, M., Malik, S., Kazai, G. (eds.) INEX 2005. LNCS, vol. 3977, pp. 254–267. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    INEX: INitiative for the Evaluation of XML Retrieval (2005),
  7. 7.
    Clarke, C.L.A.: Controlling overlap in content-oriented XML retrieval. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil, pp. 314–321 (2005)Google Scholar
  8. 8.
    Cohen, S., Mamou, J., Kanza, Y., Sagiv, Y.: XSEarch: A semantic search engine for XML. In: Proceedings of the 29th International Conference on Very Large Data Bases, Berlin, Germany, pp. 45–56 (2003)Google Scholar
  9. 9.
    Grabs, T., Schek, H.J.: ETH Zürich at INEX: Flexible information retrieval from XML with PowerDB-XML. In: Proceedings of the First Workshop of the INitiative for the Evaluation of XML Retrieval, Schloss Dagstuhl, Germany, pp. 141–148 (2002)Google Scholar
  10. 10.
    Amer-Yahia, S., Curtmola, E., Deutsch, A.: Flexible and efficient XML search with complex full-text predicates. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, Chicago, USA, pp. 575–586 (2006)Google Scholar
  11. 11.
    Liu, F., Yu, C.T., Meng, W., Chowdhury, A.: Effective keyword search in relational databases. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, Chicago, USA, pp. 563–574 (2006)Google Scholar
  12. 12.
    Theobald, M., Schenkel, R., Weikum, G.: An efficient and versatile query engine for TopX search. In: Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, pp. 625–636 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Toshiyuki Shimizu
    • 1
  • Norimasa Terada
    • 2
  • Masatoshi Yoshikawa
    • 1
  1. 1.Graduate School of InformaticsKyoto University 
  2. 2.Graduate School of Information ScienceNagoya University 

Personalised recommendations