Signs and Formal Concepts

  • Uta Priss
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2961)

Abstract

In this paper we propose a semiotic conceptual framework which is compatible with Peirce’s definition of signs and uses formal concept analysis for its conceptual structures. The goal of our research is to improve the use of formal languages such as ontology languages and programming languages. Even though there exist a myriad of theories, models and implementations of formal languages, in practice it is often not clear which strategies to use. AI ontology language research is in danger of repeating mistakes that have already been studied in other disciplines (such as linguistics and library science) years ago.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ballentyne, G.: Class notes (1992) (Unpublished manuscript)Google Scholar
  2. 2.
    Barwise, J., Seligman, J.: Information Flow. The Logic of Distributed Systems. Cambridge University Press, Cambridge (1997)MATHGoogle Scholar
  3. 3.
    Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical Foundations. Springer, Berlin (1999)MATHGoogle Scholar
  4. 4.
    McCarthy, J.: Notes on Formalizing Context. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France, pp. 555–560 (1993)Google Scholar
  5. 5.
    Orr, K.T.: Structured Systems Development. Yourdon Press, New York (1977)Google Scholar
  6. 6.
    Peirce, C.: A Fragment. CP 2.228. In: Hartshorne, Weiss (eds.) Collected papers, vol. 1-6 (1897); Burks (ed.): vol. 7-8. Harvard University Press, Cambridge (1958-1966)Google Scholar
  7. 7.
    Prechelt, L.: An empirical comparison of C, C++, Java, Perl, Python, Rexx, and TCL. IEEE Computer 33(10), 23–29 (2000)Google Scholar
  8. 8.
    Snelting, G.: Reengineering of Configurations Based on Mathematical Concept Analysis. ACM Transactions on Software Engineering and Methodology 5(2), 99–110 (1995)Google Scholar
  9. 9.
    Sowa, J.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)MATHGoogle Scholar
  10. 10.
    Star, S.L.: The structure of ill-structured solutions: Boundary objects and heterogeneous problem-solving. In: Huhns, Gasser (eds.) Distributed Artificial Intelligence, Pirman, vol. 2, pp. 37–54 (1989)Google Scholar
  11. 11.
    Wolff, K.E., Yameogo, W.: Time Dimension, Objects, and Life Tracks. A Conceptual Analysis. In: de Moor, A., Lex, W., Ganter, B. (eds.) ICCS 2003. LNCS (LNAI), vol. 2746, Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Uta Priss
    • 1
  1. 1.School of ComputingNapier University 

Personalised recommendations