Advertisement

The representation of semantic constraints in conceptual graph systems

  • Guy W. Mineau
  • Rokia Missaoui
Knowledge Representation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1257)

Abstract

The conceptual graph formalism is both simple and expressive. It offers great potential as a modeling formalism for developing information systems. In fact, its potential was recognized by the ANSI X3H4.6 committee, which recommended its adoption as a standard for such modeling tasks [1]. However, it lacks the modeling capabilities required to represent a wide range of semantic constraints, even though this is a vital characteristic of any useful modeling formalism. In this article, we propose a representation based on generalization hierarchies as defined [15], which allows most semantic constraints found in database literature to be: 1) represented in a unified framework, 2) enforced at all times, 3) subject to a minimum of resources, and 4) compared with one another in terms of their scope. Also, this paper shows that no cg system which allows the generalization of concepts, such as with maximal type expansion, is sound without the explicit representation of certain semantic constraints.

Keywords

semantic constraints conceptual graph theory semantics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    ANSI, Committee X3H4.6. IRDS Conceptual Schema. X3H4.6/92-091, 1992.Google Scholar
  2. 2.
    Campbell, L. & Creasy, P. A Conceptual Approach to Information Systems Design, Proc. of the 7th Annual Workshop on Conceptual Structures: Theory and Implementation, Las Cruces, NM, July 1992, 46–55, 1992Google Scholar
  3. 3.
    Creasy, P. ENIAM: A more Complete Conceptual Schema Language. In: Proceedings 15th International Conference on Very Large Data Bases, Amsterdam, August, 1989.Google Scholar
  4. 4.
    Creasy, P. Conceptual Graphs as Canonical Data Model. In: Supplementary Proceedings of the 2nd Int. Conf. on Conceptual Structures (ICCS-94), Maryland, August 94, 70–77, 1994.Google Scholar
  5. 5.
    Creasy, P. & Moulin, B. Approaches to Data Conceptual Modeling. In Proc. of the 6th Annual Workshop on Conceptual Structures, E. Way (Ed.), Binghamton, New York: State University of New York at Binghamton, pp. 387–399, 1991.Google Scholar
  6. 6.
    Elmasri, R. & Navathe, S.B. Fundamentals of Database Systems. 2nd edition. Benjamin Cummings, 1994.Google Scholar
  7. 7.
    Esch, J.W. Contexts as White Box Concepts. In: Supplementary Proceedings of the 1 st International Conference on Conceptual Structures (ICCS-93). G.W. Mineau, B. Moulin & J.F. Sowa (Eds.). Dept. of Computer Science, Université Laval, Quebec City, Canada. 17–29, 1993.Google Scholar
  8. 8.
    Levinson, R. Pattern Associativity and the Retrieval of Semantic Networks. Computers & Mathematics with Applications, 23(6–9), 573–600, 1992.Google Scholar
  9. 9.
    Levinson, R. A. UDS: A Universal Data Structure. In Proceedings of the Second International Conference on Conceptual Structures College Park, Maryland USA: pp. 230–250, 1994.Google Scholar
  10. 10.
    Levinson, R. A. & Ellis, G. Multi-level hierarchical retrieval. Knowledge Based Systems, 5(3), 233–244, 1992.Google Scholar
  11. 11.
    Mineau, G.W. View, Mappings and Functions: Essential Definitions to the Conceptual Graph Theory. In: W. M. Tepfenhart, J. P. Dick, & J. F. Sowa (Eds.), Conceptual Structures: Current Practices, (pp. 160–174). Springer-Verlag, 1994.Google Scholar
  12. 12.
    Moulin, B. & Creasy, P. Extending the Conceptual Graph Approach for Data Conceptual Modeling, Data & Knowledge Engineering, 8(3), 223–248, 1992.Google Scholar
  13. 13.
    Nijssen, G.M. & Halpin, T.A., Conceptual Schema and Relational Database Design: A Fact-Based Approach, Prentice-Hall, Englewood Cliffs, NJ, 1989.Google Scholar
  14. 14.
    Pfeiffer, H.D. & Hartley, R.T. Temporal, spatial, and constraint handling in the Conceptual Programming environment, CP. Journal of Experimental & Theoretical Artificial Intelligence; 4(2). 167–183, 1992.Google Scholar
  15. 15.
    Sowa, J. F. Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, 1984.Google Scholar
  16. 16.
    Sowa, J. F. Knowledge Representation in Databases, Expert Systems and Natural Language, Proceedings IFIP Working Conf. On the Role of Artificial Intelligence in Databases and Information Systems, Guangzhou, China, July, 1988.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Guy W. Mineau
    • 1
  • Rokia Missaoui
    • 2
  1. 1.Department of Computer ScienceUniversité LavalQuebec CityCanada
  2. 2.Dept. of Computer ScienceUniversité du Québec à MontréalMontrealCanada

Personalised recommendations