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KARO: An integrated environment for reusing ontologies

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 867)

Abstract

This paper shows how KARO (Knowledge Acquisition Environment with Reusable Ontologies) supports the development of the domain layer in MIKE (Model-based and Incremental Knowledge Engineering). KARO supplements the reuse of generic problem-solving methods at the task and inference layers in MIKE with a commonsense ontology at the domain layer. The intention is to make the development process easier and the final domain layer more robust.

In order to reuse ontologies powerful and integrated tools and methods are absolutely necessary. Therefore, we will describe the formal, linguistic and graphical methods, the architecture and other properties of KARO. We will enrich this survey with several examples which clarify the modeling process of the domain layer in a scheduling task using MIKE. We will show the integration of KARO and MIKE in respect of the development of the domain layer of a model of expertise. We finish the paper with a comparison of related approaches.

Keywords

Knowledge Representation Schedule Task Knowledge Element Conceptual Dependency Problem Solve Method 
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.

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References

  1. 1.
    J. Angele, D. Fensel, D. Landes, S. Neubert, and R. Studer. Model-based and Incremental Knowledge Engineering: The MIKE approach. In J. Cuena, editor, Proceedings of the IFIP TC12 Workshop on Artificial Intelligence from the Information Processing Perspective (AIFIPP'92), Madrid, Sept. 1993. Elsevier Science Publisher.Google Scholar
  2. 2.
    R. Becker and T. Pirlein. Executable design specifications through the use of knowledge-based technologies. In Proceedings of FLAIRS-94, Pensacola, Florida, May, 4–6 1994 (to appear).Google Scholar
  3. 3.
    C. Beierle. An overview on planning applications in protos-l. In Proceedings 13th IMACS World Congress on Computation and Applied Mathematics, Dublin, Ireland, 1991.Google Scholar
  4. 4.
    R. J. Brachman. The future of knowledge representation. In Proceedings Eighth National Conference on Artificial Intelligence, pages 1082–1092, Menlo Park, etc., 1990. AAAI Press / The MIT Press.Google Scholar
  5. 5.
    R. J. Brachman and J. G. Schmolze. An overview of the KL-ONE knowledge representation system. Cognitive Science, (2):171–216, 1985.Google Scholar
  6. 6.
    F. P. Brooks. No silver bullet: Essence and accidents of software engineering. IEEE Computer, 4:10–19, 1987.Google Scholar
  7. 7.
    S. Buvak and R. Fikes. Semantics of Translation, pages 12–16. Chambery, France, August, 28th. 1993.Google Scholar
  8. 8.
    D. Davidson. The logical form of action sentences. In N. Rescher, editor, The Logic of Decision and Action, pages 81–95. University of Pittsburgh Press, Pittsburgh, Pennsylvania, 1967.Google Scholar
  9. 9.
    P. Devanbu, P. Selfridge, B. Ballard, and R. Brachman. Lassie: A knowledge-based software information system. Communications of the ACM, 34(5):249–261, 1991.Google Scholar
  10. 10.
    H. Erikson, A. Puerta, and M. A. Musen. Generation of Knowledge-Acquisition Tools from Domain Ontologies, volume 93-56 of Medical Computer Science. Knowledge Systems Laboratory, Stanford, University, 1993.Google Scholar
  11. 11.
    J. Gennari. A Brief Guide to MAITRE and MODEL: An Ontology Editor and a Frame-Based Knowledge Representation Language. Number 94305–5479. 1993.Google Scholar
  12. 12.
    T. Gruber. A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2):199–221, 1993.Google Scholar
  13. 13.
    T. Gruber. Toward principles for the design of ontologies used for knowledge sharing. In N. Guarino and R. Poli, editors, Formal Ontology in Conceptual Analysis and Knowledge Representation, Dordrecht, Boston, Lancaster, Tokyo, 1994 (to appear). Kluwer Academic Publishers.Google Scholar
  14. 14.
    N. Guarino. Concepts, atrtibutes and arbitrary relations. Data and Knowledge Engineering, 8:249–261, 1992.Google Scholar
  15. 15.
    R. V. Guha and D. B. Lenat. Cyc: A midterm report. AI Magazine, (11 No. 3):32–59, 1990.Google Scholar
  16. 16.
    P. Hayes. The naive physics manifesto. In D. Michie, editor, Expert systems in the micro-electronic age, pages 243–270. Edinburgh University Press, Edinburgh, Scotland, 1978.Google Scholar
  17. 17.
    O. Herzog and C. Rollinger, editors. Text Understanding in LILOG, volume 546 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, 1991.Google Scholar
  18. 18.
    J. R. Hobbs. Ontological promiscuity. In Proceedings of the 23rd annual meeting of the Association for Computational Linguistics, pages 61–69, Chicago, Illinois, 1985. Association for Computational Linguistics.Google Scholar
  19. 19.
    G. Klose, E. Lang, and T. Pirlein, editors. The Ontology and Axioms of the LILOG knowledge base (in German), volume 307 of Informatik-Fachberichte. Springer-Verlag, Berlin, Heidelberg, New York, 1992.Google Scholar
  20. 20.
    A. Kobsa. The SB-ONE knowledge representation workbench. In Preprints of the Workshop on Formal Aspects of Semantic Networks, Two Harbors, Cal., February 1989.Google Scholar
  21. 21.
    D. Landes, D. Fensel, and J. Angele. Formalizing and operationalizing a design task with KARL. In J. Treur and T. Wetter, editors, Formal Specification of Complex Reasoning Systems. Ellis Horwood, Chicester, 1993.Google Scholar
  22. 22.
    D. Lenat and R. Guha. Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Addison-Wesley Publishing Company, Menlo Park, CA, 1990.Google Scholar
  23. 23.
    D. Lenat and R. Guha. Re: Cycling paper reviews. Artificial Intelligence, 61:149–174, 1993.Google Scholar
  24. 24.
    T. Lipkis. A KL-ONE classifier. In J. Schmolze and R. Brachman, editors, Proceedings of the 1981 KL-ONE workshop, pages 128–145. 1982.Google Scholar
  25. 25.
    J. McCarthy. Programs with common sense. In Proceedings Symposium on Mechanisation of Thought Processes I. London, 1959.Google Scholar
  26. 26.
    I. Monarch and S. Nirenburg. The role of ontology in concept acquisition for KBS. Proceedings of the First European Workshop on Knowledge Acquisition for Knowledge-Based Systems, 1987.Google Scholar
  27. 27.
    B. Nebel. Reasoning and Revision in Hybrid Representation Systems. Lecture Notes in Computer Science 422. Springer-Verlag, Berlin, Heidelberg, New York, 1990.Google Scholar
  28. 28.
    R. Neches. Book review of Lenat, Guha, D.B. and Guha, R.V.: Building large knowledgebased systems: Representation and inference in the CYC project, addison-wesley, reading, MA, 1990. Artificial Intelligence, 61, 1993.Google Scholar
  29. 29.
    S. Neubert. Model Construction in MIKE. In 7th European Knowledge Acquisition Workshop-93 (Toulouse), Lecture Notes in Artificial Intelligence, Berlin, Heidelberg, 1993. Springer Verlag.Google Scholar
  30. 30.
    E. Ostertag, J. Hendler, R. Prieto-Diaz, and C. Braun. Computing similarity in a reuse library system: An AI-based approach. ACM Transaction on Software Engineering and Methodology, 1(3):205–228, 1992.Google Scholar
  31. 31.
    C. Peltason, A. Schmiedel, K. Kindermann, and J. Quantz. The BACK system revisited, volume 75 of KIT Report. Department of Computer Science, Technische Universität Berlin, Berlin, 1989.Google Scholar
  32. 32.
    T. Pirlein. Reusing a large domain-independent knowledge base. In C. Chang, editor, Proceedings of Fifth International Conference on Software Engineering and Knowledge Engineering, pages 474–482, San Francisco Bay, CA, June 16–18 1993. Knowledge Systems Institute.Google Scholar
  33. 33.
    T. Pirlein and R. Studer. An environment for reusing ontologies within a knowledge engineering approach. In N. Guarino and R. Poli, editors, Formal Ontology in Conceptual Analysis and Knowledge Representation. Kluwer Academic Publishers, Dordrecht, Boston, Lancaster, Tokyo, 1994 (to appear).Google Scholar
  34. 34.
    U. Pletat. The knowledge representation language L-LILOG. In O. Herzog and C. Rollinger, editors, Text Understanding in LILOG, volume 546 of Lecture Notes in Artificial Intelligence, pages 357–379. Springer-Verlag, Berlin, Heidelberg, New York, 1991.Google Scholar
  35. 35.
    D. Skuce. A multi-functional knowledge management system. Knowledge Acquisition, 5(3):305–346, 1993.Google Scholar
  36. 36.
    L. Terveen and D. Wroblewski. A tool for achieving consensus in knowledge representation. In Proceedings AAAI-91, pages 74–79, Anaheim, CA, 1991.Google Scholar
  37. 37.
    B. Wielinga, A. T. Schreiber, and J. Breuker. KADS: A modelling approach to knowledge engineering. Knowledge Acquisition, 4(1):127–161, March 1992.Google Scholar
  38. 38.
    B. Wielinga and T. Schreiber. Conceptual modelling of large reusable knowledge bases. In K. Luck and H. Marburger, editors, Management and Processing of Complex Data Structures, volume 777 of LNCS, pages 181–200. Springer, Berlin, Heidelberg, New York, 1994.Google Scholar
  39. 39.
    W. A. Woods. What's in a link: Foundations for semantic networks. In D. Bobrow and A. Collins, editors, Representation and Understanding: Studies in Cognitive Science, pages 35–82. Academic Press, New York, N.Y., 1975.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  1. 1.AE Software Architectures and Technologies 2300IBM Germany DevelopmentBöblingen
  2. 2.Institute for Applied Computer Science and Formal Description TechniquesUniversity of KarlsruheKarlsruhe

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