Skip to main content
SpringerLink
Log in

Incremental Iterative Retrieval and Browsing for Efficient Conversational CBR Systems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

A case base is a repository of past experiences that can be used for problem solving. Given a new problem, expressed in the form of a query, the case base is browsed in search of “similar” or “relevant” cases. Conversational case-based reasoning (CBR) systems generally support user interaction during case retrieval and adaptation. Here we focus on case retrieval where users initiate problem solving by entering a partial problem description. During an interactive CBR session, a user may submit additional queries to provide a “focus of attention”. These queries may be obtained by relaxing or restricting the constraints specified for a prior query. Thus, case retrieval involves the iterative evaluation of a series of queries against the case base, where each query in the series is obtained by restricting or relaxing the preceding query.

This paper considers alternative approaches for implementing iterative browsing in conversational CBR systems. First, we discuss a naive algorithm, which evaluates each query independent of earlier evaluations. Second, we introduce an incremental algorithm, which reuses the results of past query evaluations to minimize the computation required for subsequent queries. In particular, the paper proposes an efficient algorithm for case base browsing and retrieval using database techniques for incremental view maintenance. In addition, the paper evaluates scalability of the proposed algorithm using its performance model. The model is created using algorithmic complexity and experimental evaluation of the system performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. P. Constantopoulos and E. Pataki, “A browser for software reuse, ” in Proc. of the CAiSE'92, edited by P. Loucopoulos, Berlin: Springer, 1992, pp. 304–326.

    Google Scholar 

  2. T.P. Martin, H.-K. Hung, and C. Walmsley, “Supporting browsing of large knowledge bases, ” Technical report, Department of Computing and Information Science, Queen's University, Kingston, ONT, 1992.

    Google Scholar 

  3. M.B. Twidale, D.M. Nichols, and C.D. Paice, “Browsing is collaborative process, ” Information Processing & Management, vol. 33, no.6, pp. 761–783, 1997.

    Google Scholar 

  4. R.J. Miller, O.G. Tsatalos, and J.H. Williams, “DataWeb: Customizable database publishing for the web, ” IEEE MultiMedia, pp. 14–21, 1997.

  5. D.W. Aha and L.A. Breslow, “Refining conversational case reasoning, ” in Proc. of the 2nd International Conference on Case-Based Reasoning, Providence, RI, 1997, pp. 267–278.

  6. K. Hammond, R. Burke, and K. Schmitt, “A case-based approach to knowledge navigation, ” in Leake, 1996, pp. 125–136, 1996.

  7. D.W. Aha, L.A. Breslow, and T. Maney, “Supporting conversational case-based reasoning in an integrated reasoning framework, ” Technical Report AIC–98–006, Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Washington, DC, 1998.

    Google Scholar 

  8. I. Jurisica and J. Glasgow, “Case-based classification using similarity-based retrieval, ” International Journal of Artificial Intelligence Tools. Special Issue of IEEE ICTAI-96 Best Papers, vol. 6, no. 4, pp. 511–536, 1997.

  9. I. Jurisica, J. Mylopoulos, J. Glasgow, H. Shapiro, and R. Casper, “Case-based reasoning in IVF: Prediction and knowledge mining, ” AI in Medicine, vol. 12, no. 1, pp. 1–24, 1998.

    Google Scholar 

  10. L. Bækgaard and L. Mark, “Incremental computation of timevarying query expressions, ” IEEE Trans. on Knowledge and Data Engineering, vol. 7, no. 4, pp. 583–589, 1995.

    Google Scholar 

  11. S. Ceri and J.Widom, “Deriving production rules for incremental view maintenance, ” in VLDB-91, Barcelona, Spain, 1991, pp. 577–589.

  12. T. Griffin and L. Libkin, “Incremental maintenance of views with duplicates, ” in ACM SIGMOD, San Jose, CA, 1995, pp. 328–339.

  13. A. Gupta, I. Mumick, and K. Ross, “Adapting materialized views after redefinitions, ” in ACM SIGMOD, San Jose, CA, 1995, pp. 211–222.

  14. D. Wettschereck and T. Dietterich, “An experimental comparison of the nearest neighbor and nearest hyperrectangle algorithms, ” Machine Learning, vol. 19, no. 1, pp. 5–27, 1995.

  15. J. Frawley and G. Piatetsky-Shapiro, Knowledge Discovery in Databases, AAAI Press, 1991.

  16. D. Aha, “Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms, ” International Journal of Man—Machine Studies, vol. 36, no. 2, pp. 267–287, 1992.

    Google Scholar 

  17. J. Ortega, “On the informativeness of the DNA promoter sequences domain theory, ” Journal of Artificial Intelligence Research, vol. 2, pp. 361–367, Research Note, 1995.

    Google Scholar 

  18. P.R. Thagard, K.J. Holyoak, G. Nelson, and D. Gotchfeld, “Analog retrieval by constraint satisfaction, ” Artificial Intelligence, vol. 46, pp. 259–310, 1990.

    Google Scholar 

  19. I. Jurisica, “Supporting flexibility. A case-based reasoning approach, ” in The AAAI Fall Symposium. Flexible Computation in Intelligent Systems: Results, Issues, and Opportunities, Cambridge, MA, 1996.

  20. B. D'Ambrosio, “Process, structure, and modularity in reasoning with uncertainty, ” in Uncertainty in Artificial Intelligence edited by R. Shachter, T. Levitt, L. Kanal, and J. Lemmer, vol. 4, pp. 15–25, 1990.

  21. E. Horvitz, “Reasoning under varying and uncertain resource constraints, ” in Proc. of AAAI-88, 1988, pp. 111–116.

  22. T. Gaasterland, “Restricting query relaxation through user constraints, ” in Proc. Int. Conf. on Intelligent and Coop. Inf. Systems, Rotterdam, 1993, pp. 359–366.

  23. F. Bancilhon, “Naive evaluation of recursively defined relations, ” Knowledge Base Management Systems, edited by M. Brodie and J. Mylopoulos, pp. 165–178, 1986.

  24. A. Gupta, I. Mumick, and V. Subrahmanian, “Maintaining views incrementally, ” in Proc. of the 12th ACM SIGACT-SIGMODSIGART Symposium on Principles of Database Systems, 1993, pp. 157–166.

  25. J.A. Blakeley, P.-A. Larson, and F.W. Tompa, “Efficiently updating materialized views, ” in ACM-SIGMOD, 1986, pp. 61–71.

  26. I. Jurisica, “TA3: Theory, implementation, and applications of similarity-based retrieval for case-based reasoning, ” Ph.D. dissertation, University of Toronto, Department of Computer Science, Toronto, Ontario, 1998.

    Google Scholar 

  27. I. Jurisica and J. Glasgow, “A case-based reasoning approach to learning control, ” in 5th Int. Conf. on Data and Knowledge Systems for Manufacturing and Engineering, DKSME-96, Phoenix, AZ, 1996.

  28. I. Jurisica, “Similarity-based retrieval for diverse Bookshelf software repository users, ” in IBM CASCON Conference, Toronto, Canada, 1997, pp. 224–235.

  29. H. Dayani-Fard and I. Jurisica, “Reverse engineering by mining dynamic repositories, ” in 5th Working Conference on Reverse Engineering (WCRE'98), Honolulu, Hawaii, 1998, pp. 174–182.

  30. I. Jurisica and B. Nixon, “Building quality into case-based reasoning systems, ” in CAiSE*98, 1998, Lecture Notes in Computer Science.

  31. B. Errico and I. Jurisica, “Adaptive agent-based systems for the Web: An application to the NECTAR project, ” in AAAI Spring Symposium on Intelligent Agents in Cyberspace, Stanford, CA: AAAI Press, 1999.

    Google Scholar 

  32. E.L. Rissland, J.J. Daniels, Z.B. Rubinstein, and D.B. Skalak, “Case-based diagnostic analysis in a blackboard architecture, ” in Proc. of AAAI-93, 1993.

  33. I.D. Watson, Applying Case-Based Reasoning: Techniques for Enterprise Systems, San Francisco, CA: Morgan Kaufmann Publishers, 1997.

    Google Scholar 

  34. R. Barletta and W. Mark, “Explanation-based indexing of cases, ” in Proc. of AAAI-88, 1988, pp. 541–546.

  35. A. Ram, “Indexing, elaboration and refinement: Incremental learning of explanatory cases, ” Machine Learning, vol. 10, no. 3, pp. 201–248, 1993.

    Google Scholar 

  36. C.M. Seifert, “Case-based learning—predictive features in indexing, ” Machine Learning, vol. 16, nos. 1, 2, pp. 37–56, 1994.

    Google Scholar 

  37. B. Smyth and M.T. Keane, “Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems, ” in Proc. of the 14th IJCAI. Montreal, Quebec, 1995, pp. 377–382.

  38. H.Watanabe, K. Okuda, and S. Fujiwara, “A strategy for forgetting cases by restricting memory, ” IEICE Trans. on Information and Systems, vol. E78D, no. 10, pp. 1324–1326, 1995.

    Google Scholar 

  39. E. Sumita, N. Nisiyama, and H. Iida, “The relationship between architectures and example-retrieval times, ” in Proc. of AAAI, Seattle, 1994, pp. 478–483.

  40. H. Shimazu, A. Shibata, and K. Nihei, “Case-based retrieval interface adapted to customer-initiated dialogues in help desk operations, ” in Proc. of the 12th National Conference on Artificial Intelligence. Seattle, WA, 1994, pp. 553–564.

  41. K.M. Gupta, “Case base engineering for large scale industrial applications, ” in AAAI Spring Symposium Series on Knowledge Management, Stanford, CA, 1997.

  42. K. Racine and Q. Yang, “Maintaining unstructured case bases, ” in Proc. of the 2nd International Conference on Case-Based Reasoning, Providence, RI, pp. 553–564, 1997.

  43. B. Smyth, “Case base maintenance, ” in 11th International Conference on Industrial Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE'98). Benicassim, Castellon, Spain, 1998, pp. 507–516.

  44. M.P. Feret and J.I. Glasgow, “Combining case-based and modelbased reasoning for the diagnosis of complex devices, ” Applied Intelligence, vol. 7, no. 1, pp. 57–78, 1997.

    Google Scholar 

  45. L.A. Breslow and D.W. Aha, “NaCoDAE: Navy conversational decision aids environment, ” Technical Report AIC–97–018, Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Washington, DC, 1997.

    Google Scholar 

  46. H. Munoz-Avila, J.A. Hendler, and D.W. Aha, “Conversational case-based planning, ” Review of Applied Expert Systems, vol. 5, pp. 163–174, 1999.

    Google Scholar 

  47. I. Jurisica and J. Glasgow, “An efficient approach to iterative browsing and retrieval for case-based reasoning, ” in 11 th International Conference on Industrial Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE'98), Benicassim, Castellon, Spain, 1998, Vol. 2 of Lecture Notes in Computer Science, LNAI 1416, pp. 535–546.

    Google Scholar 

  48. J. Luft, M. Bianca, I. Jurisica, P. Rogers, J. Glasgow, S. Fortier, and G. DeTitta, “An opening strategy for macromolecular crystallization: Case-based reasoning and the exploitation of a precipitation reaction outcome database. in Conference of the American Crystallography Association (ACA99), Buffalo, NY. An abstract for an oral presentation, 1999.

  49. J. Luft, M. Bianca, L.M. Owczarczak, D.R. Weeks, I. Jurisica, P. Rogers, J. Glasgow, S. Fortier, and G. DeTitta, “The development of high throughput methods for macromolecular microbatch crystallization, ” in Recent Advances in Macromolecular Crystallization, San Diego, CA, An abstract for an oral presentation, 1999.

  50. I. Jurisica, G. DeTitta, J. Luft, J. Glasgow, and S. Fortier, “Knowledge management in scientific domains, ” in AAAI-99 Workshop on Exploring; Synergies of Knowledge Management and Case-Based Reasoning, Orlando, FL, pp. 30–34, 1999.

  51. J. Glasgow and I. Jurisica, “Integration of case-based and imagebased reasoning, ” in AAAI'98Workshop on Case-Based Reasoning, edited by D.W. Aha, Madison, WI, pp. 67–74, 1998.

  52. J.L. Kolodner, Case-Based Reasoning, San Mateo, CA: Morgan Kaufmann, 1993.

    Google Scholar 

  53. D. Leake (ed.), Case-Based Reasoning: Experiences, lessons and future directions, AAAI Press, 1996.

  54. I.D. Watson, Applying Case-Based Reasoning: Techniques for Enterprise Systems, San Francisco, CA: Morgan Kaufmann Publishers, 1997.

    Google Scholar 

  55. R. Bergmann, S. Breen, M. Goker, M. Manago, and S. Wess, Developing Industrial Case-Based Reasoning Applications: The INRECA Methodology, Berlin: Springer, 1999.

    Google Scholar 

  56. R.S. Michalski, “Inferential theory of learning: Developing foundations for multistrategy learning, ” in Machine Learning: A Multistrategy Approach, vol. IV, 1994.

  57. A.M. Collins and R.S. Michalski, “The logic of plausible reasoning: A core theory, ” Cognitive Science, vol. 13, pp. 1–49, 1989.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Information Studies, University of Toronto, 140 St. George St., Toronto, ON M5S 3G6, Canada

    Igor Jurisica

  2. Department of Computing and Information Science, Queen's University, Kingston, ON K7L 3N6, Canada

    Janice Glasgow

  3. Department of Computer Science, University of Toronto, 6 King's College Rd., Toronto, ON M5S 3H5, Canada

    John Mylopoulos

Authors
  1. Igor Jurisica
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Janice Glasgow
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Mylopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Jurisica, I., Glasgow, J. & Mylopoulos, J. Incremental Iterative Retrieval and Browsing for Efficient Conversational CBR Systems. Applied Intelligence 12, 251–268 (2000). https://doi.org/10.1023/A:1008375309626

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1008375309626

Search

Navigation