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Formal Concept Analysis as a Support Technique for CBR

  • Belén Díaz-Agudo
  • Pedro A. González-Calero
Conference paper

Abstract

This paper shows how the use of Galois lattices and Formal Concept Analysis (FCA) can support CBR application designers, in the task of discovering knowledge embedded in the cases. FCA applied on a case library provides an internal sight of the conceptual structure and allows finding patterns, regularities and exceptions among the cases. Moreover, it extracts certain dependence rules between the attributes describing the cases, that will be used to guide the query formulation process.

Keywords

Formal Concept Description Logic Formal Context Formal Concept Analysis Hasse Diagram 
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.

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References

  1. [1]
    Althoff, K.-D., Auriol, E., Barletta R, & Manago, M., 1995. A Review of Industrial Case-Based Reasoning Tools, AI Intelligence, Oxford UK.Google Scholar
  2. [2]
    Bergmann, R, Wilke, W., Vollrath, I., & Stefan, W., 1996. “Integrating General Knowledge with Object-Oriented Case Representation and Reasoning”. In Procs. of the 4th German Workshop on CBRGoogle Scholar
  3. [3]
    Birkhoff, G., 1973. Lattice Theory, third editon. American Math. Society Coll. Publ. 25, Providence, RI.Google Scholar
  4. [4]
    Diaz-Agudo, B., & González-Calero, P. A., 2000. “An Architecture For Knowledge Intensive CBR Systems”. In Procs. of the 5th European Workshop on CBR (EWCBR’ 00).Google Scholar
  5. [5]
    Mac Gregor, R, & Bates, R., 1987. “The Loom Knowledge Representation Language”. ISI Reprint Series, ISI/RS-87-188, Univ. of Southern California.Google Scholar
  6. [6]
    Ganter, B., & Wille, R, 1997. Formal Concept Analysis. Mathematical Foundations. ISBN 3-540-62771-5 Springer Verlag.Google Scholar
  7. [7]
    Gómez-Albarran, M., Gonzalez-Calero, P.A., Diaz-Agudo, B., & Fernández-Conde, C, 1999: “Modelling the CBR Life Cycle Using Description Logics”, in Case-Based Reasoning Research and Development: Third International Conference on Case-Based Reasoning, LNAI 1650, SpringerGoogle Scholar
  8. [8]
    Porter, B.W., 1989. “Similarity Assessment: computation vs. representation”. In Procs. of DARPA CBR Workshop, Morgan Kaufmann.Google Scholar
  9. [9]
    Prediger, S., & Stumme, G., 1999. “Theory driven logical scaling”. In Procs. of the International DLs Workshop, CEUR Workshop Vol. 22.Google Scholar
  10. [10]
    Lenz, M., 1993. “CABATA-A hybrid CBR system”. In Procs. of the 1st European Workshop on CBR (EWCBR’93).Google Scholar
  11. [11]
    Wille, R., 1982. “Restructuring Lattice Theory: an approach based on hierarchies of concepts”. In Rival, I., (ed.), Ordered Sets.Google Scholar
  12. [12]
    Wille, R., 1992. “Conceptual Lattices and conceptual knowledge systems”. Computers and Mathematics with Apps. Vol. 23, No. 6–9, pp. 493–515.Google Scholar

Copyright information

© Springer-Verlag London 2001

Authors and Affiliations

  • Belén Díaz-Agudo
    • 1
  • Pedro A. González-Calero
    • 1
  1. 1.Dep. Sistemas Informáticos y ProgramaciónUniversidad Complutense de MadridSpain

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