Case Retrieval Reuse Net (CR2N): An Architecture for Reuse of Textual Solutions

  • Ibrahim Adeyanju
  • Nirmalie Wiratunga
  • Robert Lothian
  • Somayajulu Sripada
  • Luc Lamontagne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5650)


This paper proposes textual reuse as the identification of reusable textual constructs in a retrieved solution text. This is done by annotating a solution text so that reusable sections are identifiable from those that need revision. We present a novel and generic architecture, Case Retrieval Reuse Net (CR2N), that can be used to generate these annotations to denote text content as reusable or not. Obtaining evidence for and against reuse is crucial for annotation accuracy, therefore a comparative evaluation of different evidence gathering techniques is presented. Evaluation on two domains of weather forecast revision and health & safety incident reporting shows significantly better accuracy over a retrieve-only system and a comparable reuse technique. This also provides useful insight into the text revision stage.


Case Grouping Case Base Reasoning Retrieval Accuracy Textual Unit Natural Language Generation 
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.
    Brüninghaus, S., Ashley, K.D.: Reasoning with textual cases. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS, vol. 3620, pp. 137–151. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AICom 7, 39–59 (1994)Google Scholar
  3. 3.
    Plaza, E., Arcos, J.L.: Constructive adaptation. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS, vol. 2416, pp. 306–320. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Gervás, P., Díaz-Agudo, B., Peinado, F., Hervás, R.: Story plot generation based on CBR. In: Twelveth Conference on Applications and Innovations in Intelligent Systems. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Recio-García, J.A., Díaz-Agudo, B., González-Calero, P.A.: Textual cbr in jcolibri: From retrieval to reuse. In: Proceedings of the ICCBR 2007 Workshop on Textual CBR: Beyond Retrieval, pp. 217–226 (2007)Google Scholar
  6. 6.
    Lamontagne, L., Lapalme, G.: Textual reuse for email response. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS, vol. 3155, pp. 234–246. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Lenz, M., Burkhard, H.D.: Case retrieval nets: Basic ideas and extensions. In: Görz, G., Hölldobler, S. (eds.) KI 1996. LNCS, vol. 1137, pp. 227–239. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  8. 8.
    Lenz, M., Burkhard, H.D.: Case retrieval nets: Foundations, properties, implementations and results. Technical report, Humboldt University, Berlin (1996)Google Scholar
  9. 9.
    Chakraborti, S., Lothian, R., Wiratunga, N., Orecchioni, A., Watt, S.: Fast case retrieval nets for textual data. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS, vol. 4106, pp. 400–414. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Adeyanju, I., Wiratunga, N., Lothian, R., Sripada, S., Craw, S.: Solution reuse for textual cases. In: 13th UK Workshop on Case-Based Reasoning, pp. 54–62. CMS Press, University of Greenwich (2008)Google Scholar
  11. 11.
    Brüninghaus, S., Ashley, K.D.: Evaluation of textual cbr approaches. In: Proceedings of the AAAI 1998 Workshop on Textual CBR, pp. 30–34. AAAI Press, Menlo Park (1998)Google Scholar
  12. 12.
    Díaz-Agudo, B., González-Calero, P.A., Recio-García, J.A., Sánchez, A.: Building cbr systems with jcolibri. Special Issue on Experimental Software and Toolkits of the Journal Science of Computer Programming 69, 68–75 (2007)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Sripada, S.G., Reiter, E., Hunter, J., Yu, J.: Sumtime-meteo: Parallel corpus of naturally occurring forecast texts and weather data. Technical Report AUCS/TR0201, Department of Computer Science, University of Aberdeen (2002)Google Scholar
  14. 14.
    Massie, S., Wiratunga, N., Craw, S., Donati, A., Vicari, E.: From anomaly reports to cases. In: Weber, R.O., Richter, M.M. (eds.) ICCBR 2007. LNCS, vol. 4626, pp. 359–373. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Mudambi-Ananthasayanam, R., Wiratunga, N., Chakraborti, S., Massie, S., Khemani, D.: Evaluation measures for TCBR systems. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS, vol. 5239, pp. 444–458. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998), zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ibrahim Adeyanju
    • 1
  • Nirmalie Wiratunga
    • 1
  • Robert Lothian
    • 1
  • Somayajulu Sripada
    • 2
  • Luc Lamontagne
    • 3
  1. 1.School of ComputingRobert Gordon UniversityAberdeenScotland, UK
  2. 2.Department of Computing ScienceUniversity of AberdeenAberdeenScotland, UK
  3. 3.Department of Computer Science and Software EngineeringUniversité Laval, Québec(Québec)Canada

Personalised recommendations