Fast Case Retrieval Nets for Textual Data

  • Sutanu Chakraborti
  • Robert Lothian
  • Nirmalie Wiratunga
  • Amandine Orecchioni
  • Stuart Watt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4106)


Case Retrieval Networks (CRNs) facilitate flexible and efficient retrieval in Case-Based Reasoning (CBR) systems. While CRNs scale up well to handle large numbers of cases in the case-base, the retrieval efficiency is still critically determined by the number of feature values (referred to as Information Entities) and by the nature of similarity relations defined over the feature space. In textual domains it is typical to perform retrieval over large vocabularies with many similarity interconnections between words. This can have adverse effects on retrieval efficiency for CRNs. This paper proposes an extension to CRN, called the Fast Case Retrieval Network (FCRN) that eliminates redundant computations at run time. Using artificial and real-world datasets, it is demonstrated that FCRNs can achieve significant retrieval speedups over CRNs, while maintaining retrieval effectiveness.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lenz, M., Burkhard, H.-D.: Case Retrieval Nets: Basic Ideas and Extensions. KI, 227–239 (1996)Google Scholar
  2. 2.
    Chakraborti, S., Ambati, S., Balaraman, V., Khemani, D.: Integrating Knowledge Sources and Acquiring Vocabulary for Textual CBR. In: Proc. of the 8th UK CBR Workshop, pp. 74–84 (2003)Google Scholar
  3. 3.
    Lenz, M., Burkhard, H.: Case Retrieval Nets: Foundations, Properties, Implementation, and Results, Technical Report, Humboldt-Universität zu, Berlin (1996)Google Scholar
  4. 4.
    Lenz, M.: Knowledge Sources for Textual CBR Applications, Textual CBR: Papers from the 1998 Workshop Technical Report WS-98-12, pp. 24–29. AAAI Press (1998)Google Scholar
  5. 5.
    Balaraman, V., Chakraborti, S.: Satisfying Varying Retrieval Requirements in Case-Based Intelligent Directory Assistance. In: Proc. of the FLAIRS Conference (2004)Google Scholar
  6. 6.
    Lenz, M.: Case Retrieval Nets Applied to Large Case-Bases. In: Proc. 4th German Workshop on CBR, Informatik Preprints, Humboldt-Universität zu, Berlin (1996)Google Scholar
  7. 7.
    Lenz, M., Auriol, E., Manago, M.: Diagnosis and Decision Support. In: Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (eds.) Case-Based Reasoning Technology. LNCS, vol. 1400, pp. 51–90. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  8. 8.
    Rijsbergen, C.J.: Information Retrieval, 2nd edn. Butterworths, London (1979)Google Scholar
  9. 9.
    Delany, S.J., Cunningham, P., Tsymbal, A., Coyle, L.: A Case-based Technique for Tracking Concept Drift in Spam Filtering. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 3–16. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Lenz, M., Burkhard, H.-D.: CBR for Document Retrieval - The FAllQ Project. In: Leake, D.B., Plaza, E. (eds.) ICCBR 1997. LNCS, vol. 1266. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  11. 11.
    Chakraborti, S., Watt, S., Wiratunga, N.: Introspective Knowledge Acquisition in Case Retrieval Networks for Textual CBR. In: Proc. of the 9th UK CBR Workshop, pp. 51–61 (2004)Google Scholar
  12. 12.
    Wilson, D., Bradshaw, S.: CBR Textuality. In: Proc. of the Fourth UK Case-Based Reasoning Workshop, pp. 67–80 (1999)Google Scholar
  13. 13.
    Lytinen, S.L., Tomuro, N.: The Use of Question Types to Match Questions in FAQFinder, Mining Answers From Texts and Knowledge Bases, AAAI Technical Report SS-02-06, pp. 46–53. AAAI Press (2002)Google Scholar
  14. 14.
    Lenz, M.: Case Retrieval Nets as a Model for Building Flexible Information Systems, Ph.D dissertation, Humboldt Uni. Berlin. Faculty of Mathematics and Natural Sciences (1999)Google Scholar
  15. 15.
    Lenz, M., Hubner, A., Kunje, M.: Textual CBR. In: Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (eds.) Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400, pp. 115–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Yang, Y., Pederson, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proc. of the International Conference on Machine Learning, pp. 412–420 (1997)Google Scholar
  17. 17.
    Kolodner, J.L.: Case-Based Reasoning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  18. 18.
    Schaaf, J.W.: “Fish and Sink”: An Anytime Algorithm to Retrieve Adequate Cases. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 371–380. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  19. 19.
    Weβ, S., Althoff, K.-D., Derwand, G.: Using k-d trees to Improve the Retrieval Step in Case-Based Reasoning. In: Wess, S., Richter, M., Althoff, K.-D. (eds.) EWCBR 1993. LNCS, vol. 837, pp. 167–181. Springer, Heidelberg (1994)Google Scholar
  20. 20.
    Rumelhart, D.E., McClelland, J.L.: PDP Research Group. In: Parallel distributed Processing: Explorations in the Microstructure of Cognition. Foundations, vol. 1. MIT Press, Cambridge (1986)Google Scholar
  21. 21.
    Wolverton, M.: An Investigation of Marker Passing Algorithms for Analogue Retrieval. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 359–370. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  22. 22.
    Wolverton, M., Hayes-Roth, B.: Retrieving Semantically Distant Analogies with Knowledge-Directed Spreading Activation. In: Proc. AAAI 1994 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sutanu Chakraborti
    • 1
  • Robert Lothian
    • 1
  • Nirmalie Wiratunga
    • 1
  • Amandine Orecchioni
    • 1
  • Stuart Watt
    • 1
  1. 1.School of ComputingThe Robert Gordon UniversityAberdeenScotland, UK

Personalised recommendations