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Developing Knowledge-Based Systems with MIKE

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Abstract

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.

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References

  • Angele, J. 1993. Operationalisierung des Modells der Expertise mit KARL. Ph.D. thesis, University of Karlsruhe, Infix-Verlag, St. Augustin.

    Google Scholar 

  • 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.

  • 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.

  • 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.

  • 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.

  • Boehm, B.W. 1988. A spiral model of software development and enhancement. IEEE Computer, 61-72.

  • Breuker, J. and Van de Velde, W. (Eds.) 1994. The CommonKADS Library for Expertise Modelling. Amsterdam, The Netherlands: IOS Press.

    Google Scholar 

  • 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 

  • Clancey, W.J. 1983. The epistemology of a rule-based expert system—A framework for explanation. Artificial Intelligence, 20:215-251.

    Google Scholar 

  • Dardenne, A., van Lamsweerde, A., and Fickas, S. 1993. Goal-directed requirements acquisition. Science of Computer Programming, 20:3-50.

    Google Scholar 

  • 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.

  • de Kleer, J., Mackworth, A.K., and Reiter, R. 1992. Characterizing diagnoses and systems. Artificial Intelligence, 56.

  • 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 

  • 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 

  • 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.

  • Fensel, D. 1995. The Knowledge Acquisition and Representation Language KARL. Boston: Kluwer Academic Publishers.

    Google Scholar 

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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).

  • 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 

  • 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.

  • 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.

  • 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).

  • 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.

  • 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 

  • Gissel, A. and Knauth, P. 1997. Knowledge-based support for the participatory design and implementation of shift systems. Shiftwork International Newsletter, (14):1, 9.

  • Gruber, T.R. 1993. A translation approach to portable ontology specifications, Knowledge Acquisition, 5(2): 199-221.

    Google Scholar 

  • Hayes-Roth, F., Waterman, D.A., and Lenat, D.B. (Eds.). 1983. Building Expert Systems. Addison-Wesley Publisher.

  • 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 

  • 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.

  • 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 

  • 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.

  • 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.

  • 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 

  • 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.

  • 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 

  • 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.

  • Moran, T.P. and Carroll, J.M. 1996. Design Rationale—Concepts, Techniques, and Use. Mahwah: Erlbaum.

    Google Scholar 

  • Morik, K. 1987. Sloppy modeling. In K. Morik (Ed.), Knowledge Representation and Organisation in Machine Learning, LNAI 347, Springer.

  • 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 

  • 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.

  • 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 

  • Newell, A. 1982. The knowledge level, Artificial Intelligence, 18:87-127.

    Google Scholar 

  • 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.

  • Pirlein, Th. and Studer, R. 1997. Integrating the reuse of commonsense ontologies and problem-solving methods, Institute AIFB, Research Report 354, University of Karlsruhe.

  • 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 

  • 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 

  • 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.

  • 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 

  • Puppe, F. 1993. Systematic Introduction to Expert Systems: Knowledge Representation and Problem-Solving Methods. Springer.

  • Rumbaugh, J., Blaha, M., Premerlani, W., Eddy, F., and Lorensen, W. 1991. Object-Oriented Modeling and Design. Prentice Hall.

  • 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 

  • 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 

  • 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.

  • Sutcliffe, A.G. and Maiden, N.A.M. 1994. Domain modeling for reuse. Proc. 3rd Int. Conf. on Software Reuse, Rio de Janeiro.

  • Ullman, J.D. 1988. Principles of Database and Knowledge-Base Systems, vol. I. Rockville, MD: Computer Sciences Press.

    Google Scholar 

  • v. Harmelen, F. and Balder, J. 1992. (ML)2: A formal language for KADS conceptual models, Knowledge Acquisition, 4(1).

  • 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).

  • VanLehn, K. 1989. Problem-solving and cognitive skill acquisition. In M.I. Posner (Ed.), Foundations of Cognitive Science, Cambridge: The MIT Press.

    Google Scholar 

  • Waarle, H. 1995. Knowledge-based system modeling using MIKE. Swiss PTT, R&D Department, Technical Report.

  • Wielinga, B.J. and Breuker, J.A. 1984. Interpretation models for knowledge acquisition. Proc. of the European Conf. on AI (ECAI-84), Pisa.

  • Wielinga, B., Schreiber, G., and Breuker, J.A. 1992. KADS: A modeling approach to knowledge engineering, Knowledge Acquisition, 4(1).

  • Yourdon, E. 1989. Modern Structured Analysis. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

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  1. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    J. Angele, D. Fensel, D. Landes & R. Studer

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  1. J. Angele
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Angele, J., Fensel, D., Landes, D. et al. Developing Knowledge-Based Systems with MIKE. Automated Software Engineering 5, 389–418 (1998). https://doi.org/10.1023/A:1008653328901

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