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Defining knowledge layers for textual case-based reasoning

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Advances in Case-Based Reasoning (EWCBR 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1488))

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Abstract

Textual CBR applications deal with problems that have traditionally been addressed by the Information Retrieval community, namely the handling of textual documents. Since CBR is an AI technique, the questions arise as to what kind of knowledge may enhance the system, where this knowledge comes from, and how it contributes to the performance of such a system. We will address these questions in this paper by showing how the various pieces of knowledge available in a specific domain can be utilized.

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References

  1. R. Burke, K. Hammond, V. Kulyukin, S. Lytinen, N. Tomuro, and S. Schoenberg. Question Answering from Frequently Asked Question Files. AI Magazine, 18(2):57–66, 1997.

    Google Scholar 

  2. J. J. Daniels and E. L. Rissland. What You Saw Is What You Want: Using Cases to Seed Information. In Leake and Plaza [5], pages 325–336.

    Google Scholar 

  3. B. Glasgow, A. Mandell, D. Binney, L. Ghemri, and D. Fisher. MITA: An information extraction approach to the analysis of free-form text in life insurance application. AI Magazine, 19(1):59–71, 1998.

    Google Scholar 

  4. M. Kunze and A. Hübner. CBR on Semi-structured Documents: The Experience-Book and the FA11Q Project. In Proceedings 6th German Workshop on CBR, 1998.

    Google Scholar 

  5. D. B. Leake and E. Plaza, editors. Case-Based Reasoning Research and Development, Proceedings ICCBR-97, Lecture Notes in Artificial Intelligence, 1266. Springer Verlag, 1997.

    Google Scholar 

  6. M. Lenz and H.-D. Burkhard. CBR for Document Retrieval — The FAllQ Project. In Leake and Plaza [5], pages 84–93.

    Google Scholar 

  7. M. Lenz, H.-D. Burkhard, P. Pirk, E. Auriol, and M. Manago. CBR for Diagnosis and Decision Support. AI Communications, 9(3):138–146, 1996.

    Google Scholar 

  8. G. A. Miller. Wordnet: A lexical database for english. Communications of the ACM, 38(11):39–41, 1995.

    Article  Google Scholar 

  9. M. M. Richter. The knowledge contained in similarity measures. Invited Talk at ICCBR-95, 1995. http://wwwagr.informatik.uni-kl.de/~lsa/CBR/Richtericcbr95remarks.html.

    Google Scholar 

  10. C. J. v. Rijsbergen. Information Retrieval. Butterworth-Heinemann, London, 2 edition, 1979.

    MATH  Google Scholar 

  11. E. Riloff and W. Lehnert. Information extraction as a basis for high-precision text classification. ACM Transactions on Information Systems, 12(3):296–333, 1994.

    Article  Google Scholar 

  12. G. Salton and M. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, New York, 1983.

    MATH  Google Scholar 

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Barry Smyth Pádraig Cunningham

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© 1998 Springer-Verlag Berlin Heidelberg

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Lenz, M. (1998). Defining knowledge layers for textual case-based reasoning. In: Smyth, B., Cunningham, P. (eds) Advances in Case-Based Reasoning. EWCBR 1998. Lecture Notes in Computer Science, vol 1488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056342

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  • DOI: https://doi.org/10.1007/BFb0056342

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64990-8

  • Online ISBN: 978-3-540-49797-4

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