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KARaCAs: Knowledge Acquisition with Repertory Grids and Formal Concept Analysis for Dialog System Construction

  • Hilke Garbe
  • Claudia Janssen
  • Claus Möbus
  • Heiko Seebold
  • Holger de Vries
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4248)

Abstract

We describe a new knowledge acquisition tool that enabled us to develop a dialog system recommending software design patterns by asking critical questions. This assistance system is based on interviews with experts. For the interviews we adopted the repertory grid method and integrated formal concept analysis. The repertory grid method stimulates the generation of common and differentiating attributes for a given set of objects. Using formal concept analysis we can control the repertory grid procedure, minimize the required expert judgements and build an abstraction based hierarchy of design patterns, even from the judgements of different experts. Based on the acquired knowledge we semi-automatically generate a Bayesian Belief Network (BBN), that is used to conduct dialogs with users to suggest a suitable design pattern for their individual problem situation. Integrating these different methods into our knowledge acquisition tool KARaCAs enables us to support the entire knowledge acquisition and engineering process. We used KARaCAs with three design pattern experts and derived approximately 130 attributes for 23 design patterns. Using formal concept analysis we merged the three lattices and condensed them to approximately 80 common attributes.

Keywords

Knowledge Acquisition Design Pattern Problem Situation Concept Lattice Formal Context 
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.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Software. Addision-Wesley, Reading (1995)Google Scholar
  2. 2.
    Alexander, C., Ishikawa, S., Silverstein, M., Jacobson, M., Fiksdahl-King, I., Angel, S.: A Pattern Language. Oxford University Press, New York (1977)Google Scholar
  3. 3.
    Meffert, K.: Supporting design patterns with annotations. In: 13th Annual IEEE International Symposium and Workshop on Engineering of Computer Based Systems (ECBS 2006), pp. 437–445 (2006)Google Scholar
  4. 4.
    Gomes, P.: Software design retrieval using bayesian networks and wordnet. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 184–197. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Möbus, C., Seebold, H., Garbe, H.: A greedy knowledge acquisition method for the rapid prototyping of knowledge structures. In: Clark, P., Schreiber, G. (eds.) Proceedings of the 3rd International Conference on Knowledge Capture, pp. 211–212. ACM Press, New York (2005)CrossRefGoogle Scholar
  6. 6.
    Kelly, G.A.: Psychology of Personal Constructs. W. W. Norton, New York (1955)Google Scholar
  7. 7.
    Delugach, H., Lampkin, B.: Troika: Using grids, lattices and graphs in knowledge acquisition. In: Stumme, G. (ed.) Working with Conceptual Structures: Contributions to ICCS 2000, pp. 201–214. Shaker, Aachen (2000)Google Scholar
  8. 8.
    Gaines, B., Shaw, M.: Knowledge acquisition tools based on personal construct psychology. The Knowledge Engineering Review 8(1), 49–85 (1993)CrossRefGoogle Scholar
  9. 9.
    Castro-Schez, J.J., Jennings, N.R., Luo, X., Shadbolt, N.: Acquiring domain knowledge for negotiating agents: a case study. International Journal of Human-Computer Studies 61(1), 3–31 (2004)CrossRefGoogle Scholar
  10. 10.
    Spangenberg, N., Wolff, K.: Datenreduktion durch die Formale Begriffsanalyse von Repertory Grids. In: Einführung in die Repertory Grid-Technik, Band 2, Klinische Forschung und Praxis, pp. 38–54. Verlag Hans Huber, Bern (1993)Google Scholar
  11. 11.
    Richards, D.: Ripple-down rules with formal concept analysis: A comparison to personal construct psychology. In: Gaines, B., Musen, M. (eds.) Proceedings of 11th Workshop on Knowledge Acquisition, Modeling and Management, Banff Canada. SRDG Publications, Calgary (1998)Google Scholar
  12. 12.
    Choisel, S., Wickelmaier, F.: Extraction of auditory features and elicitation of attributes for the assessment of multichannel reproduced sound. In: 118th Convention of the Audio Engineering Society, Barcelona, Spain (2005)Google Scholar
  13. 13.
    Bruder, R., Lengnink, K., Prediger, S.: Ein Instrumentarium zur Erfassung subjektiver Theorien über Mathematikaufgaben. Preprint Nr. 2265 des Fachbereichs Mathematik, TU Darmstadt (2003)Google Scholar
  14. 14.
    Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical Foundations. Springer, Heidelberg (1999)zbMATHGoogle Scholar
  15. 15.
    Cimiano, P., Hotho, A., Stumme, G., Tane, J.: Conceptual knowledge processing with formal concept analysis and ontologies. In: Eklund, P. (ed.) ICFCA 2004. LNCS (LNAI), vol. 2961, pp. 189–207. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Kollewe, W.: Begriffliche Wissensverarbeitung: Wie Begriffsstrukturen die Pflege und Recherche in Wissensdatenbanken unterstützen. In: Bitkom KnowTech. (2002)Google Scholar
  17. 17.
    Düwel, S.: BASE - ein begriffsbasiertes Analyseverfahren für die Software-Entwicklung. PhD thesis, Philipps-Universität Marburg (2000)Google Scholar
  18. 18.
    Lengnink, K., Prediger, S.: Development of the personal constructs about mathematical tasks - a qualitative study using repretory grid methodology. In: Proceedings of the 27th Annual Meeting of the International Group for the Psychology of Mathematics Education (PME), Hawaii (2003)Google Scholar
  19. 19.
    Stumme, G., Maedche, A.: FCA - MERGE: Bottom-up merging of ontologies. In: IJCAI, pp. 225–234 (2001)Google Scholar
  20. 20.
    Ganter, B., Stumme, G.: Creation and merging of ontology top-levels. In: Ganter, B., de Moor, A., Lex, W. (eds.) ICCS 2003. LNCS, vol. 2746, pp. 131–145. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  21. 21.
    Stumme, G.: Ontology merging with formal concept analysis. In: Kalfoglou, Y., Schorlemmer, M., Sheth, A., Staab, S., Uschold, M. (eds.) Semantic Interoperability and Integration. Number 04391 in Dagstuhl Seminar Proceedings, Internationales Begegnungs- und Forschungszentrum (IBFI), Schloss Dagstuhl, Germany (2005)Google Scholar
  22. 22.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Revised second printing edn. Morgan Kaufman Publishers, San Mateo (1998)Google Scholar
  23. 23.
    Jensen, F.: Bayesian Networks and Decision Graphs, Statistics for Engineering and Information Science. Springer, Berlin (2001)Google Scholar
  24. 24.
    Folckers, J., Möbus, C., Schröder, O., Thole, H.J.: An intelligent problem solving environment for designing explanation models and for diagnostic reasoning in probabilistic domains. In: Lesgold, A.M., Frasson, C., Gauthier, G. (eds.) ITS 1996. LNCS, vol. 1086, pp. 353–362. Springer, Heidelberg (1996)Google Scholar
  25. 25.
    Mislevy, R., Almond, R.G., Yan, D., Steinberg, L.: Bayes nets in educational assessment: Where do the numbers come from? CSE Technical Report 518, Center for the Study of Evaluation, University of California, Los Angeles (2000)Google Scholar
  26. 26.
    Bunt, A., Conati, C.: Assessing effective exploration in open learning environments using bayesian networks. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 698–707. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  27. 27.
    Zapata-Rivera, J., Greer, J.: Student model accuracy using inspectable bayesian student models. In: Hoppe, U., Verdejo, F., Kay, J. (eds.) Artificial Intelligence in Education: Shaping the Future of Learning through Intelligent Technologies, pp. 65–72. IOS Press, Amsterdam (2003)Google Scholar
  28. 28.
    Schachter, R.D.: Bayes-ball: The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams). In: Cooper, G.F., Moral, S. (eds.) Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI 1998), pp. 480–487. Morgan Kaufmann, San Francisco (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hilke Garbe
    • 1
  • Claudia Janssen
    • 2
  • Claus Möbus
    • 1
  • Heiko Seebold
    • 2
  • Holger de Vries
    • 2
  1. 1.University of OldenburgGermany
  2. 2.OFFIS OldenburgGermany

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