Integrating frames, rules and uncertainty in a database-coupled knowledge-representation system

  • Petra Drescher
  • Martin Holeňa
  • Rainer Kruschinski
  • Gernod Laufkötter
Integration of Databases and Expert Systems
Part of the Lecture Notes in Computer Science book series (LNCS, volume 856)


This paper describes a knowledge-representation system IFS (Intelligent Framework Services) being developed as a part of the JESSI Common Frame project. The system is based on a combination of frames and rules, which are integrated through the object-oriented view with multiple approaches to uncertainty processing, and coupled to an object-oriented database.


knowledge representation object-oriented paradigm frames rules uncertain knowledge uncertainty processing KBS - DB coupling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Stewart M. Clamen. Schema evolution and integration. International Journal of Parallel and Distributed Databases, 2(1):101–126, 1994.Google Scholar
  2. 2.
    Umeshwar Dayal, Alejandro P. Buchmann, and Dennis R. McCarthy. Rules are objects too: A knowledge model for an active object-oriented database system. In Proceedings of (AOODB88), pages 129–143, 1988.Google Scholar
  3. 3.
    F. di Primio and K.H. Wittur. Babylon: A meta interpretation modell for handling mixed knowledge representations. In Proceedings of 7th International Workshop on Expert Systems and their Applications, pages 821–833, 1987.Google Scholar
  4. 4.
    P. Drescher, R. Kruschinski, and G. Laufkötter. Report on hybrid knowledge representation, Deliverable IDKM-7 Nr. 064. Technical report, JCF-ESPRIT Project 7364, Cadlab, 1993.Google Scholar
  5. 5.
    P. Frasconi, M. Gori, M. Maggini, and G. Soda. Unified integration of explicit knowledge and learning by example in recurrent networks. IEEE TDKE, 8, 1993.Google Scholar
  6. 6.
    H.W. Güsgen, U. Junker, and A. Voß. Constraints in a hybrid knowledge representation system. In Proceedings of the IJCAI, pages 30–33, 1987.Google Scholar
  7. 7.
    P. Hájek, T. Havránek, and R. Jiroušek. Uncertain Information Processing in Expert Systems. Kluwer Academic Publishers, Dordrecht, 1992.Google Scholar
  8. 8.
    P. Hájek and J.J. Valdes. Algebraic foundations of uncertainty processing in rule-based expert systems (group-theoretical approach). CAI, 9:325–347, 1990.Google Scholar
  9. 9.
    P. Hájek and J.J. Valdes. Generalized algebraic foundations of uncertainty processing in rule-based expert systems (dempsteroids). CAI, 10:29–56, 1991.Google Scholar
  10. 10.
    M. Holeňa. Theoretical principles of uncertainty processing in expert systems. Technical report, JCF-ESPRIT Project 7364, Cadlab, 1993, 57 pages.Google Scholar
  11. 11.
    M. Holeňa and R. Kruschinski. Specification of HyKL 1.0 description language for hybrid knowledge representation and management. Technical report, JCF — ESPRIT Project 7364, Cadlab, 1993, 62 pages.Google Scholar
  12. 12.
    Meichun Hsu and Thomas E. Cheatham Jr. Rule execution in cplex: A persistent objectbase. In Proceedings of (AOODB88), pages 150–161, 1988.Google Scholar
  13. 13.
    Setrag N. Koshafian and George P. Copeland. Object identity. In Proceedings of (OOPSLA86), pages 406–416, 1986.Google Scholar
  14. 14.
    S. Laufman, editor. Standard for a Frame-Based Knowledge Representation, volume Draft 2.1b. P1252 Working Group, 1993.Google Scholar
  15. 15.
    Daniel P. Miranker. TREAT: A New and Efficient Match Algorithm for AI Production Systems. Morgan Kaufmann Publishers, Inc., San Mateo, California, 1990.Google Scholar
  16. 16.
    B. Nebel and K. von Luck. Issues of integration and balancing in hybrid knowledge representation systems. In K. Morik, editor, Proceedings of the GWAI, pages 115–123, 1987.Google Scholar
  17. 17.
    Siemens Nixdorf Informationssyteme AG 1993, Universität Paderborn, Delft University of Technology. JCF V3.0 — Development System: Object Management System, Administrators Guide, preliminiary edition, 1993.Google Scholar
  18. 18.
    Andrea H. Skarra and Stanley B. Zdonik. The management of changing types in an object-oriented database. In Proceedings of the (OOPSLA86), pages 483–495, 1986.Google Scholar
  19. 19.
    J.D. Ullman. Principles of Database Systems. Computer Science Press, 1982.Google Scholar
  20. 20.
    J. Whittaker. Graphical Models in Applied Multivariate Statistics. John Wiley and Sons, New York, 1990.Google Scholar
  21. 21.
    H.J. Zimmermann. Fuzzy Set Theory and its Applications. Kluwer Academic Pub-lishers, Dordrecht, 2. edition, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Petra Drescher
    • 1
  • Martin Holeňa
    • 1
  • Rainer Kruschinski
    • 1
  • Gernod Laufkötter
    • 1
  1. 1.Cadlab - Joint Research & Development InstituteUniversity of Paderborn and Siemens Nixdorf InformationssystemePaderbornGermany

Personalised recommendations