Specifying and Reasoning about Business Rules in a Semantic Network

  • Leora Morgenstern
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 371)


Semantic networks have long been recognized as useful in com-monsense reasoning and business applications. This paper explores an attempt to capture a set of business rules as a semantic network. While some of these rules can be expressed as nodes and their connecting links, other rules are best thought of as logical formulae that are attached to particular nodes. We define Augmented Semantic Networks, a formal structure that can capture this concept. We discuss several of the problems that arise in Augmented Semantic Networks and suggest solutions to these problems.


Semantic Network Boolean Expression Business Rule Service Cover Default Theory 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [ER92]
    Proceedings of the First International Conference on Enterprise Modelling Technology, MIT Press, 1990Google Scholar
  2. [E88]
    D. Etherington: Reasoning with Incomplete Information, Morgan Kaufmann, Los Altos, 1988MATHGoogle Scholar
  3. [GP90]
    M. Gelfond and H. Przymusinska: Formalization of Inheritance Reasoning in Autoepistemic Logic, Fundamenta Informaticae, 1990Google Scholar
  4. [G91]
    B. Grosof: Generalizing Prioritization, in J. Allen, R. Fikes, and E. Sandewall (eds): Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning, Morgan Kaufmann, San Mateo, 1991, 289–300Google Scholar
  5. [H94]
    J. Horty: Some Direct Theories of Nonmonotonic Inheritance in D. Gabbay, C. Hogger, and J. Robinson, eds: Handbook of Logic in Artificial Intelligence and Logic Programming, Vol. 3: Nonmonotonic Reasoning and Uncertain Reasoning, Oxford University Press, Oxford, 1994, 111–187Google Scholar
  6. [HT90]
    J. Horty and R. Thomason: Boolean Extensions of Inheritance Networks, AAAI 1990, Morgan Kaufmann, Los Altos, 1990, 663–669Google Scholar
  7. [HTH90]
    J. Horty, R. Thomason and D. Touretzky: A Skeptical Theory of Inheritance in Nonmonotonic Semantic Networks, Artificial Intelligence 42, 1990, 311–349CrossRefGoogle Scholar
  8. [K91]
    H. Kautz: A Formal Theory of Plan Recognition and its Implementation in J. Allen, H. Kautz, R. Pelavin, and J. Tenenberg: Reasoning About Plans, Morgan Kaufman, San Mateo, 1991, 69–125Google Scholar
  9. [KS91]
    H. Kautz and B. Selman: Hard Problems for Simple Default Logics in R. Brachman, H. Levesque, and R. Reiter (eds): Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, Morgan Kaufman, San Mateo, 1991, 189–197Google Scholar
  10. [KS96]
    H. Kilov and I. Simmonds: Business patterns: Reusable Abstract Constructs for Business Specifications, to appear in Proceedings, IFIPWG8.3 Conference, London, July 1996, Chapman and HallGoogle Scholar
  11. [K96]
    H. Kilov, Private communicationGoogle Scholar
  12. [EMW 91]
    E. Mays, R. Dionne, and R. Weida: K-REP System Overview, SIGART Bulletin, 2:3, 1991CrossRefGoogle Scholar
  13. [M80]
    J. McCarthy: Circumscription — A Form of Non-Monotonic Reasoning, Artificial Intelligence 13, 1980, 27–39MATHCrossRefGoogle Scholar
  14. [M86]
    J. McCarthy: Applications of Circumscription to Formalizing Common-sense Knowledge, Artificial Intelligence 28, 1986, 86–116CrossRefGoogle Scholar
  15. [M82]
    D. McDermott: A Temporal Logic for Reasoning About Processes and Plans, Cognitive Science 6, 1982, 101–155CrossRefGoogle Scholar
  16. [M75]
    M. Minsky: “A Framework for Representing Knowledge,” in The Psychology of Computer Vision, ed. P.H. Winston, McGraw Hill, New York, 1975, 211–277Google Scholar
  17. [Q68]
    M. Quillian: Semantic Memory in M. Minsky (ed): Semantic Information Processing, MIT Press, Cambridge, 1968Google Scholar
  18. [R80]
    R. Reiter: A Logic for Default Reasoning, Artificial Intelligence 13, 1980Google Scholar
  19. [RC81]
    R. Reiter and G. Criscuolo: On Interacting Defaults, IJCAI 1981, 270–276Google Scholar
  20. [SL83]
    J. Schmolze and T. Lipkis: Classification in the KL-ONE Representation System, IJCAI 1983 Google Scholar
  21. [SL93]
    B. Selman and H. Levesque: The Complexity of Path-Based Defeasible Inheritance, Artificial Intelligence 62:2, 1993, 303–340MATHCrossRefGoogle Scholar
  22. [S88]
    Y. Shoham: Reasoning About Change: Time and Causation from the Standpoint of Artificial Intelligence, MIT Press, Cambridge, 1988Google Scholar
  23. [S84]
    J. Sowa: Conceptual Structures: Information Processing in Mind and Machine, Addison-Wesley, Reading, 1984MATHGoogle Scholar
  24. [T86]
    D. Touretzky: The Mathematics of Inheritance Systems, Morgan Kaufmann, Los Altos, 1986MATHGoogle Scholar
  25. [W75]
    W. Woods: “What’s in a Link,” in D. Bobrow and A. Collins, eds: Representation and Understanding, Academic Press, New York, 1975, 35–82Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Leora Morgenstern
    • 1
  1. 1.IBM T.J. Watson Research CenterYorktown Heights

Personalised recommendations