Skip to main content
SpringerLink
Log in

An integrated system for knowledge sharing among heterogeneous knowledge derivation systems

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Solving problems in a complex application domain often requires a seamles integration of some existing knowledge derivation systems which have been independently developed for solving subproblems using different inferencing schemes. This paper presents the design and implementation of an Integrated Knowledge Derivation System (IKDS) which allows the user to query against a global database containing data derivable by the rules and constraints of a number of cooperative heterogeneous systems. The global knowledge representation scheme, the global knowledge manipulation language and the global knowledge processing mechanism of IKDS are described in detail. For global knowledge representation, the dynamic aspects of knowledge such as derivational relationships and restrictive dependencies among data items are modeled by a Function Graph to uniformly represent the capabilities (or knowledge) of the rule-based systems, while the usual static aspects such as data items and their structural interrelationships are modeled by an object-oriented model. For knowledge manipulation, three types of high-level, exploratory queries are introduced to allow the user to query the global knowledge base. For deriving the best global answers for queries, the global knowledge processing mechanism allows the rules and constraints in different component systems to be indiscriminately exploited despite the incompatibilities in their inferencing mechanisms and interpretation schemes. Several key algorithms required for the knowledge processing mechanism are described in this paper. The main advantage of this integration approach is that rules and constraints can in effect be shared among heterogeneous rule-based systems so that they can freely exchange their data and operate as parts of a single system. IKDS achieves the integration at the rule level instead of at the system level. It has been implemented in C running in a network of heterogenous component systems which contain three independently developed expert systems with different rule formats and inferencing mechanisms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. Missikoff et al., “Toward unified approach to expert and database systems,” in Proc. of the First Int'l Work-shop on Expert Database Systems, Kiawah Island, SC, 1984.

  2. M. Bordie and M. Jarke, “On integrating logic programming and databases,” in Proc. of the First Int'l Workshop on Expert Database Systems, Kiawah Island, SC, 1984.

  3. S. Tsur, “LDL-A technology for the realization of tightly coupled expert database systems,” IEEE Expert, pp. 41–51, Fall 1988.

  4. E. Wong et al., “Enhancing INGRES with deductive power,” in Proc. of the First Int'l Workshops on Expert Database Systems, Kiawah Island, SC, 1984.

  5. G. Wilson et al., “Distributed database considerations in an expert system for radar analysis intelligent database interfaces,” in Proc. of the First Int'l Workshop on Expert Database Systems, Kiawah Island, SC, 1984, pp. 586–602.

  6. L. Kerschberg and A. Shepherd, “Constraint management in expert database systems,” in Proc. of the First Int'l Workshop on Expert Database Systems, Kiawah Island, SC, 1984.

  7. L. Raschid, “The design and implementation techniques for an integrated knowledge base management system,” Ph.D. Dissertation, Department of Electrical Engineering, University of Florida, 1987.

  8. C. Freksa, “Knowledge representation for interactive aircraft design,” in Proc. of the Tech. Assessment and Management Conf. of the Gottlieb Duttweiler Institute, April 1985, pp. 221–230.

  9. C. Troeder and H. Naumann, “Expert systems of optimal selection of machine elements,” in Proc. of the Tech. Assessment and Management Conf. of the Gottlieb Duttweiler Institute, April 1985, pp. 207–214.

  10. R. Bannister and M. Moore, “General rotational machinery expert system,” in IEEE Western Conf. on Expert Systems, 1987, pp. 140–151.

  11. M. Taie and S. Srihari, “Device modeling for fault diagosis,” in Expert Systems in Government Symposium, 1986, pp. 144–150.

  12. S. Ceri and G. Pelagatti, Distributed Database-Principles and Systems, New York: McGraw-Hill, pp. 361–385, 1984.

    Google Scholar 

  13. C. Date, Introduction to Database Systems, 2nd ed. Reading, MA: Addison-Wesley, 1977.

    Google Scholar 

  14. D. Heimbigner and D. McLeod, “A federated architecture for information management,” ACM TOOLS, vol. 3, no. 3, July 1985.

  15. T. Landers and R. Rosenberg, “An overview of MULTIBASE,” in Distributed Databases, edited by H. Schneider, Amsterdam: North-Holland, 1977.

    Google Scholar 

  16. J. Rothnie et al., “Introduction to a system for distributed databases (SDD-1),” ACM TODS, vol. 5, no. 1, 1980.

  17. A. Ferrier and C. Stangret, “Heterogeneity in the distributed database management system SRi-USDELTA,” in Eighth VLDB, Mexico City, 1983.

  18. B. Czejdo, “An approach to schema integration and query formulation in federated database systems,” in Proc. of the Third Int'l Conf. on Data Engineering, 1987, pp. 36–45.

  19. F. Golshani, “Tools for the construction of expert database systems,” in Proc. of the First Int'l Workshop on Expert Database Systems, Kiawah Island, SC, 1984, pp. 442–445.

  20. M. Jarke and Y. Vassiliou, “Coupling expert systems and database management systems,” in Artificial Intelligence Applications for Business, Norwood, NJ: Ablex, 1984.

    Google Scholar 

  21. C. Kellog, “Knowledge management: A practical amalgam of knowledge and database technology,” in Proc. of the National Conf. on Artificial Intelligence, Carnegie-Mellon University, PA, 1982.

  22. S. Su and L. Rashid, “Incorporating knowledge rules in semantic data model: An approach to integrated knowledge management,” in IEEE Conf. on Artificial Intelligence Applications, Miami, December 1985.

  23. N. Sridharan, “1986 workshop on distributed AI,” AI Magazine, pp. 75–85, Fall 1987.

  24. R. Smith and N. Davis, “Frameworks for cooperation in distributed problem solving,” IEEE Trans. Systems, Man, Cybernetics, vol. SMC-11, no. 1, January 1981.

  25. V. Lesser and D. Corkill, “The distributed vehicle monitoring testbed,” AI Magazine, pp. 15–33, Fall 1983.

  26. P. Gerring and E. Shortliffe, “The interviewer/reasoner model: An approach to improving system responsiveness in interactive AI systems,” AI Magazine, pp. 24–27, Fall 1982.

  27. S. Subramanyan, J. Park, and S. Su, “An integrated hetorogeneous expert system: Design and implementation,” to appear in the proceedings of the Third PROCIEM Conference, Orlando, November 14–16, 1990.

  28. D. Tomada et al., “A framework of expert system with strategic knowledge,” in Proc. of Int'l Conf. on Data Engineering, IEEE, 1986, pp. 236–243.

  29. S. Navathe, S. Gala, and S. Geum, “A federated approach to loosely-coupled integration of multiple information systems,” University of Florida, DBS R/D Center, Tech. Report, April 1989.

  30. P. Lyngbaek and D. McLeod, “Object sharing in distributed information systems,” ACMTOOIS, vol. 2, no. 2, pp. 96–122, April 1984.

    Google Scholar 

  31. D. Heimbigner, “A federated architecture for database systems,” Ph.D. Dissertation, University of Southern California, Los Angeles, August 1982.

  32. J. Larson et al., “ATOZ—A prototype intelligent interface to multiple information systems,” in Proc. of the IFIP Working Conference, Rome, September 1985.

  33. R. Marti, “Integrating database and program descriptions using an ER dictionary,” Journal of Systems and Software, vol. 4, nos. 2 and 3, pp. 185–195, July 1984.

    Google Scholar 

  34. W. Kaemmer and J. Larson, “A graph-oriented representation and unification technique for automatic selecting and invoking software functions,” in Proc. of AAAI, 1986, pp. 825–830.

  35. B. Thompson, “Knowledge + control = expert systems,” AI Expert, vols. 1–3, pp. 25–29, November 1986.

    Google Scholar 

  36. L. Brownston et al., Programming Expert Systems in OPS5, Reading, MA: Addison-Wesley, pp. 51–60, 1985.

    Google Scholar 

  37. B. Buchanan and E. Shortliffe, Rule-Based Expert Systems, Reading, MA: Addison-Wesley, pp. 51–70, 1985.

    Google Scholar 

  38. H. Sakai, “A method for entity-relationship behavior modeling,” in Entity-Relationship Approach to Software Engineering, edited by C. Davis et al., Elsevier Science Publishers B V. (North-Holland), pp. 111–129, 1983.

    Google Scholar 

  39. M. Brodie, “On modeling behavioral semantics of data-based,” in Proc. 7th VLDB, pp. 32–42, 1981.

  40. J. Park and S. Su, “An integrated approach to heterogeneous expert systems,” in Proc. of the Third International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, vol. 2, Charleston, SC, 1990, pp. 583–592.

  41. S. Su, V. Krishnamurthy, and H. Lam, “An object-oriented semantic association model (OSAM*),” in Artificial Intelligence: Manufacturing Theory and Practice, edited by S. Kumara et al., Industrial Engineering and Management Press, Narcross, GA. 1989.

    Google Scholar 

  42. J. Park, “An integrated approach to knowledge sharing among heterogeneous rule-based systems,” Ph.D. Dissertation, University of Florida, Gainesville, August 1990.

  43. A. Aho, J. Hopcroft, and J. Ullman, The Design and Analysis of Computer Algorithms, Reading, MA: Addison-Wesley, pp. 176–195, 1974.

    Google Scholar 

  44. P. Held, “Extending the capabilities of database management systems to support the needs of expert systems,” Ph.D. Dissertation, University of Minnesota, 1988.

  45. P. Jackson, Introduction to Expert Systems, Reading, MA: Addison-Wesley, pp. 29–51, 1986.

    Google Scholar 

  46. S. Su et al., “The architecture and prototype implementation of an integrated manufacturing database system,” in Proc. of the COMPCON, Spring 1986.

  47. J. Peterson and A. Silberschatz, Operating System Concepts, 2nd ed., Reading, MA: Addison-Wesley, pp. 307–308, 1985.

    Google Scholar 

Download references

Authors
  1. Stanley Y. W. Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jong H. Park
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Database Systems Research and Development Center, Department of Computer Information Sciences, Department of Electrical Engineering, University of Florida

Rights and permissions

Reprints and permissions

About this article

Cite this article

Su, S.Y.W., Park, J.H. An integrated system for knowledge sharing among heterogeneous knowledge derivation systems. Appl Intell 1, 223–245 (1991). https://doi.org/10.1007/BF00118998

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00118998

Key words

Search

Navigation