Automated Software Engineering

, Volume 5, Issue 4, pp 389–418 | Cite as

Developing Knowledge-Based Systems with MIKE

  • J. Angele
  • D. Fensel
  • D. Landes
  • R. Studer


The paper describes the MIKE (Model-based and Incremental Knowledge Engineering) approach for developing knowledge-based systems. MIKE integrates semiformal and formal specification techniques together with prototyping into a coherent framework. All activities in the building process of a knowledge-based system are embedded in a cyclic process model. For the semiformal representation we use a hypermedia-based formalism which serves as a communication basis between expert and knowledge engineer during knowledge acquisition. The semiformal knowledge representation is also the basis for formalization, resulting in a formal and executable model specified in the Knowledge Acquisition and Representation Language (KARL). Since KARL is executable, the model of expertise can be developed and validated by prototyping. A smooth transition from a semiformal to a formal specification and further on to design is achieved because all the description techniques rely on the same conceptual model to describe the functional and nonfunctional aspects of the system. Thus, the system is thoroughly documented at different description levels, each of which focuses on a distinct aspect of the entire development effort. Traceability of requirements is supported by linking the different models to each other.

knowledge engineering knowledge acquisition knowledge-based systems domain modeling task modeling problem-solving method 


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  1. Angele, J. 1993. Operationalisierung des Modells der Expertise mit KARL. Ph.D. thesis, University of Karlsruhe, Infix-Verlag, St. Augustin.Google Scholar
  2. Angele, J. 1996. Conceptual modeling in KARL and G-KARL. Proc. of the CASE Workshop during the 15th Int. Conf. on Conceptual Modelling (ER-96), Cottbus, Germany.Google Scholar
  3. Angele, J., Decker, S., Perkuhn, R., and Studer, R. 1996a. Modeling problem-solving methods in new KARL. Proc. of the 10th Knowledge Acquisition for Knowledge-Based Systems Workshop (KAW'96), Banff, Canada.Google Scholar
  4. Angele, J., Fensel, D., and Studer, R. 1996b. Domain and task modelling in MIKE. In A. Sutcliffe et al. (Eds.), Domain Knowledge for Interactive System Design, Chapman & Hall, 1996.Google Scholar
  5. Benjamins, R., Fensel, D., and Straatman, R. 1996. Assumptions of problem-solving methods and their role in knowledge engineering. Proc. of the 12th European Conf. on Artificial Intelligence (ECAI-96), Budapest.Google Scholar
  6. Boehm, B.W. 1988. A spiral model of software development and enhancement. IEEE Computer, 61-72.Google Scholar
  7. Breuker, J. and Van de Velde, W. (Eds.) 1994. The CommonKADS Library for Expertise Modelling. Amsterdam, The Netherlands: IOS Press.Google Scholar
  8. Chandrasekaran, B., Johnson, T.R., and Smith, J.W. 1992. Task structure analysis for knowledge modeling. Communications of the ACM, 35(9):124-137.Google Scholar
  9. Clancey, W.J. 1983. The epistemology of a rule-based expert system—A framework for explanation. Artificial Intelligence, 20:215-251.Google Scholar
  10. Dardenne, A., van Lamsweerde, A., and Fickas, S. 1993. Goal-directed requirements acquisition. Science of Computer Programming, 20:3-50.Google Scholar
  11. Decker, S., Daniel, M., Erdmann, M., and Studer, R. 1997. An enterprise reference scheme for integrating model-based knowledge engineering and enterprise modeling. Proc. of the 10th European Workshop on Knowledge Acquisition, Modeling, and Management (EKAW-97), LNAI, Springer-Verlag.Google Scholar
  12. de Kleer, J., Mackworth, A.K., and Reiter, R. 1992. Characterizing diagnoses and systems. Artificial Intelligence, 56.Google Scholar
  13. Eriksson, H. 1992. A survey of knowledge acquisition techniques and tools and their relationship to software engineering. Journal of Systems and Software, 19:97-107.Google Scholar
  14. Farquhar, A., Fikes, R., and Rice, J. 1997. The Ontolingua server: A tool for collaborative ontology construction. Int. J. of Human-Computer Studies (IJHCS), 46(6):707-728.Google Scholar
  15. Feigenbaum, E.A. 1977. The art of artificial intelligence: Themes and case studies of knowledge engineering. Proc. of the Int. Joint Conf. on AI (IJCAI-77), pp. 1014-1029.Google Scholar
  16. Fensel, D. 1995. The Knowledge Acquisition and Representation Language KARL. Boston: Kluwer Academic Publishers.Google Scholar
  17. Fensel, D. 1997a. An ontology-based broker: Making problem-solving method reuse work. Proc. of the Workshop on Problem-Solving Methods for Knowledge-based Systems at the 15th Int. Joint Conf. on AI (IJCAI-97), Nagoya, Japan.Google Scholar
  18. Fensel, D. 1997b. The tower-of-adapter method for developing and reusing problem-solving methods. Proc. of European Knowledge Acquisition Workshop (EKAW-97), LNAI, Springer-Verlag.Google Scholar
  19. Fensel, D. and Groenboom, R. 1997. Specifying knowledge-based systems with reusable components. Proc. of the 9th Int. Conf. on Software Engineering and Knowledge Engineering (SEKE-97), Madrid, Spain.Google Scholar
  20. Fensel, D. and Schönegge, A. 1997. Specifying and verifying knowledge-based systems with KIV. Proc. of the European Symposium on the Validation and Verification of Knowledge Based Systems EUROVAV-97, Leuven Belgium.Google Scholar
  21. Fensel, D. and Straatman, R. 1996. The essence of problem-solving methods: Making assumptions for efficiency reasons. In N. Shadbolt et al. (Eds.), Advances in Knowledge Acquisition, LNAI 1076, Springer-Verlag.Google Scholar
  22. Fensel, D. and van Harmelen, F. 1994. A comparison of languages which operationalize and formalize KADS models of expertise, The Knowledge Engineering Review, 9(2).Google Scholar
  23. Fensel, D., Angele, J., Landes, D., and Studer, R. 1993. Giving structured analysis techniques a formal and operational semantics with KARL. In Züllighoven et al. (Eds), Requirements Engineering' 93: Prototyping, Stuttgart: Teubner-Verlag.Google Scholar
  24. Fensel, D., Angele, J., and Studer, R. (a). The knowledge acquisition and representation language KARL, to appear in IEEE Transactions on Knowledge and Data Engineering.Google Scholar
  25. Fensel, D., Erdmann, M., and Studer, R. 1997. Ontology groups: Semantically enriched subnets of the WWW. Proc. of the Int.Workshop Intelligent Information Integration during the 21st German Annual Conf. on Artificial Intelligence, Freiburg, Germany.Google Scholar
  26. Fensel, D., Groenboom, R., and Renardel de Lavalette, G.R. (b). MCL: Specifying the reasoning of knowledge-based systems, to appear in Data and Knowledge Engineering (DKE).Google Scholar
  27. Fichtner, W., Landes, D., Spengler, Th., Ruch, M., Rentz, O., and Studer, R. 1995. Der Mike Ansatz zur Modellierung von Expertenwissen im Umweltbereich-dargestellt am Beispiel des Bauschuttrecyclings. In H. Kremers et al. (Eds.), Space and Time in Environmental Information Systems, Proc. of the 9th Int. Symposium on Computer Science for Environmental Protection, Berlin, Metropolis-Verlag.Google Scholar
  28. Gennari, J., Tu, S., Rothenfluh, Th., and Musen, M. 1994. Mapping domains to methods in support of reuse, Int. J. of Human-Computer Studies (IJHCS), 41:399-424.Google Scholar
  29. Gissel, A. and Knauth, P. 1997. Knowledge-based support for the participatory design and implementation of shift systems. Shiftwork International Newsletter, (14):1, 9.Google Scholar
  30. Gruber, T.R. 1993. A translation approach to portable ontology specifications, Knowledge Acquisition, 5(2): 199-221.Google Scholar
  31. Hayes-Roth, F., Waterman, D.A., and Lenat, D.B. (Eds.). 1983. Building Expert Systems. Addison-Wesley Publisher.Google Scholar
  32. Harel, D. 1984. Dynamic logic. In D. Gabby et al. (Eds.), Handbook of Philosophical Logic, vol. II, Extensions of Classical Logic. Dordrecht, NL: Publishing Company, pp. 497-604.Google Scholar
  33. Jarke, M., Bubenko, J., Rolland, C., Sutcliffe, A., and Vassiliou, Y. 1993. Theories underlying requirements engineering: An overview of NATURE at genesis. Proc. IEEE Symposium on Requirements Engineering, San Diego.Google Scholar
  34. Kifer, M., Lausen, G., and Wu, J. 1995. Logical foundations of object-oriented and frame-based languages, Journal of the ACM, 42:741-843.Google Scholar
  35. Knight, K. and Luk, S. 1994. Building a large knowledge base for machine translation. Proc. of the American Ass. for Artificial Intelligence Conf. (AAAI'94), Seattle.Google Scholar
  36. Landes, D. 1994. DesignKARL—A language for the design of knowledge-based systems. Proc. of the 6th Int. Conf. on Software Engineering and Knowledge Engineering (SEKE'94), Jurmala, Latvia.Google Scholar
  37. Landes, D. and Studer, R. 1994. Mechanisms for structuring knowledge-based systems. In D. Karagiannis (Ed.), Database and Expert System Applications, Lecture Notes in Computer Science 856, Springer, Berlin, pp. 488-497.Google Scholar
  38. Landes, D. and Studer, R. 1995. The treatment of non-functional requirements in MIKE. In W. Schäfer and P. Botella (Eds.), Proc. of the 5th European Software Engineering Conf. (ESEC'95), Sitges, Spain, Springer, Berlin, Lecture Notes in Computer Science 989.Google Scholar
  39. Lenat, D.B. and Guha, R.V. 1990. Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project. Addison-Wesley Publ. Co., Inc., Reading, MA.Google Scholar
  40. Lee, J. 1991. Extending the potts and bruns model for recording design rationale. Proc. 13th Int. Conf. on Software Engineering (ICSE'91), Austin, USA, pp. 114-125.Google Scholar
  41. Moran, T.P. and Carroll, J.M. 1996. Design Rationale—Concepts, Techniques, and Use. Mahwah: Erlbaum.Google Scholar
  42. Morik, K. 1987. Sloppy modeling. In K. Morik (Ed.), Knowledge Representation and Organisation in Machine Learning, LNAI 347, Springer.Google Scholar
  43. Mylopoulos, J., Chung, L., and Nixon, B. 1992. Representing and using non-functional requirements: A process-oriented approach, IEEE Transactions on Software Engineering, 18(6):483-497.Google Scholar
  44. Neubert, S. 1993. Model construction in MIKE (Model-based and incremental knowledge engineering). In N. Aussenac et al. (Eds.), Knowledge Acquisition for Knowledge-Based Systems, Proc. of the 7th European Workshop (EKAW'93), Toulouse, France, LNAI 723, Springer-Verlag.Google Scholar
  45. Nebel, B. 1996. Artificial intelligence: A computational perspective. In G. Brewka (Ed.), Principles of Knowledge Representation, CSLI Publications, Studies in Logic, Language and Information, Stanford.Google Scholar
  46. Newell, A. 1982. The knowledge level, Artificial Intelligence, 18:87-127.Google Scholar
  47. Pirlein, T. and Studer, R. 1994. KARO: An integrated environment for reusing ontologies. In Steels et al. (Eds), A Future of Knowledge Acquisition, Proc. 8th European Knowledge Acquisition Workshop (EKAW'94), Hoegaarden, LNCS 867, Springer.Google Scholar
  48. Pirlein, Th. and Studer, R. 1997. Integrating the reuse of commonsense ontologies and problem-solving methods, Institute AIFB, Research Report 354, University of Karlsruhe.Google Scholar
  49. Poeck, K., Fensel, D., Landes, D., and Angele, J. 1996. Combining KARL and CRLM for designing vertical transportation systems. Int. J. of Human-Computer Studies (IJHCS), 44(3-4):435-467.Google Scholar
  50. Pohl, K., Starke, G., and Peters, P. 1995. Workshop Summary First International Workshop on Requirements Engineering: Foundation of Software Quality (REFSQ'94), ACM SIGSOFT, 20(1):39-45.Google Scholar
  51. Potts, C. and Bruns, G. 1988. Recording the reasons for design decisions. Proc. 10th Int. Conf. on Software Engineering ICSE'88, Singapore, pp. 418-427.Google Scholar
  52. Przymusinski, T.C. 1988. On the declarative semantics of deductive databases and logic programs. In J. Minker (Ed.), Foundations of Deductive Databases and Logic Programming, Los Altos, CA: Morgan Kaufmann Publisher.Google Scholar
  53. Puppe, F. 1993. Systematic Introduction to Expert Systems: Knowledge Representation and Problem-Solving Methods. Springer.Google Scholar
  54. Rumbaugh, J., Blaha, M., Premerlani, W., Eddy, F., and Lorensen, W. 1991. Object-Oriented Modeling and Design. Prentice Hall.Google Scholar
  55. Schreiber, G. 1993. Operationalizing models of expertise. In G. Schreiber, B. Wielinga, and J. Breuker (Eds.), KADS—A Principled Approach to Knowledge-Based Systems Development, London: Academic Press, pp. 119- 149.Google Scholar
  56. Schreiber, A.Th., Wielinga, B., Akkermans, J.M., Van De Velde, W., and de Hoog, R. 1994. CommonKADS. A comprehensive methodology for KBS development, IEEE Expert, 9(6):28-37.Google Scholar
  57. Studer, R., Eriksson, H., Gennari, J., Tu, S., Fensel, D., and Musen, M. 1996. Ontologies and the configuration of prolem-solving methods. Proc. of the 10th Knowledge Acquisition for Knowledge-based Systems Workshop (KAW-96), Banff.Google Scholar
  58. Sutcliffe, A.G. and Maiden, N.A.M. 1994. Domain modeling for reuse. Proc. 3rd Int. Conf. on Software Reuse, Rio de Janeiro.Google Scholar
  59. Ullman, J.D. 1988. Principles of Database and Knowledge-Base Systems, vol. I. Rockville, MD: Computer Sciences Press.Google Scholar
  60. v. Harmelen, F. and Balder, J. 1992. (ML)2: A formal language for KADS conceptual models, Knowledge Acquisition, 4(1).Google Scholar
  61. van Heijst, G., Schreiber, A.T., and Wielinga, B.J. 1997. Using explicit ontologies in knowledge-based system development, Int. J. of Human-Computer Interaction (IJHCI), 46(6).Google Scholar
  62. VanLehn, K. 1989. Problem-solving and cognitive skill acquisition. In M.I. Posner (Ed.), Foundations of Cognitive Science, Cambridge: The MIT Press.Google Scholar
  63. Waarle, H. 1995. Knowledge-based system modeling using MIKE. Swiss PTT, R&D Department, Technical Report.Google Scholar
  64. Wielinga, B.J. and Breuker, J.A. 1984. Interpretation models for knowledge acquisition. Proc. of the European Conf. on AI (ECAI-84), Pisa.Google Scholar
  65. Wielinga, B., Schreiber, G., and Breuker, J.A. 1992. KADS: A modeling approach to knowledge engineering, Knowledge Acquisition, 4(1).Google Scholar
  66. Yourdon, E. 1989. Modern Structured Analysis. Englewood Cliffs: Prentice-Hall.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • J. Angele
    • 1
  • D. Fensel
    • 1
  • D. Landes
    • 1
  • R. Studer
    • 1
  1. 1.Institute AIFBUniversity of KarlsruheKarlsruheGermany

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