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Using ontologies for defining tasks, problem-solving methods and their mappings

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Knowledge Acquisition, Modeling and Management (EKAW 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1319))

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Abstract

In recent years two main technologies for knowledge sharing and reuse have emerged: ontologies and problem solving methods (PSMs). Ontologies specify reusable conceptualizations which can be shared by multiple reasoning components communicating during a problem solving process. PSMs describe in a domain-independent way the generic reasoning steps and knowledge types needed to perform a task. Typically PSMs are specified in a task-specific fashion, using modelling frameworks which describe their control and inference structures as well as their knowledge requirements and competence. In this paper we discuss a novel approach to PSM specification, which is based on the use of formal ontologies. In particular our specifications abstract from control, data flow and other dynamic aspects of PSMs to focus on the logical theory associated with a PSM (method ontology). This approach concentrates on the competence and knowledge requirements of a PSM, rather than internal control details, thus enabling black-box-style reuse. In the paper we also look at the nature of PSM specifications and we show that these can be characterised in a task-independent style as generic search strategies. The resulting ‘modelling gap’ between method-independent task specifications and task-independent method ontologies can be bridged by constructing the relevant adapter ontology, which reformulates the method ontology in task-specific terms. An important aspect of the ontology-centred approach described here is that, in contrast with other characterisations of task-independent PSMs, it does away with the simple, binary distinction between weak and strong methods. We argue that any method can be defined in either task-independent or task-dependent style and therefore such distinction is of limited utility in PSM reuse. The differences between PSMs which affect reuse concern the ontological commitments which they make with respect to domain knowledge and goal specifications.

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References

  1. J. M. Akkermans, B. Wielinga, and A. TH. Schreiber: Steps in Constructing Problem-Solving Methods. In N. Aussenac et al. (eds.): Knowledge-Acquisition for Knowledge-Based Systems, Lecture Notes in AI, no 723, Springer-Verlag, 1993.

    Google Scholar 

  2. J. Angele, D. Fensel, and R. Studer: Domain and Task Modelling in MIKE. In A. Sutcliffe et al. (eds.), Domain Knowledge for Interactive System Design, Chapman & Hall, 1996.

    Google Scholar 

  3. R. Benjamins: Problem Solving Methods for Diagnosis And Their Role in Knowledge Acquisition, International Journal of Expert Systems: Research and Application, 8(2):93–120, 1995.

    Google Scholar 

  4. P. Beys, R. Benjamins, and G. van Heijst: Remedying the Reusability-Usability Tradeoff for Problem-solving Methods. In Proceedings of the 10th Banff Knowledge Acquisition for Knowledge-Based System Workshop (KAW 96), Banff, Canada, November 9–14, 1996.

    Google Scholar 

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

    Google Scholar 

  6. T. Bylander, and B. Chandrasekaran: Generic Tasks in Knowledge-Based Reasoning: The Right Level of Abstraction for Knowledge Acquisition. In B. Gaines et al. (eds.), Knowledge Acquisition for Knowledge-Based Systems, vol 1, pp. 65–77. Academic Press, London, 1988.

    Google Scholar 

  7. B. Chandrasekaran: Design Problem Solving: A Task Analysis. AI Magazine, 11(4):59–71, Winter Issue, 1990.

    Google Scholar 

  8. B. Chandrasekaran, T.R. Johnson, and J. W. Smith: Task Structure Analysis for Knowledge Modeling, Communications of the ACM, 35(9): 124–137, 1992.

    Article  Google Scholar 

  9. H. Eriksson, Y. Shahar, S. W. Tu, A. R. Puerta, and M. A. Musen: Task Modeling with Reusable Problem-Solving Methods, Artificial Intelligence, 79(2):293–326, 1995.

    Article  Google Scholar 

  10. A. Farquhar, R. Fickas, and J. Rice: The Ontolingua Server: a Tool for Collaborative Ontology Construction, International Journal of Human-Computer Studies (IJHCS), 46(6):707–728, 1997.

    Article  Google Scholar 

  11. D. Fensel: An Ontology-based Broker: Making Problem-Solving Method Reuse Work. In Proceeedings of the Workshop on Problem-Solving Methods for Knowledge-based Systems (W26) during IJCAI-97, Japan, August 23, 1997.

    Google Scholar 

  12. D. Fensel: The Tower-of-Adapters Method for Developing and Reusing Problem-Solving Methods. To appear in Proceedings of European Knowledge Acquisition Workshop (EKAW-97), LNAI, Springer-Verlag, 1997.

    Google Scholar 

  13. D. Fensel and R. Groenboom: Specifying Knowledge-Based Systems with Reusable Components. In Proceedings of the 9th International Conference on Software Engineering & Knowledge Engineering (SEKE-97), Madrid, Spain, June 18–20, 1997.

    Google Scholar 

  14. D. Fensel and A. Schönegge: Specifying and Verifying Knowledge-Based Systems with KIV. In Proceedings of the European Symposium on the Validation and Verification of Knowledge Based Systems EUROVAV-97, Leuven Belgium, June 26–28, 1997.

    Google Scholar 

  15. D. Fensel, H. Eriksson, M. A. Musen, and R. Studer: Developing Problem-Solving by Introducing Ontological Commitments, International Journal of Expert Systems: Research & Applications, vol 9(4), 1996.

    Google Scholar 

  16. Gennari, J. H., Tu, S. W., Rothenfluh, T. E., Musen, M. A. Mapping Domains to Methods in Support of Reuse. In Proceedings of the 8th Banff Knowledge Acquisition Workshop (KAW-94), Banff, Canada, 1994.

    Google Scholar 

  17. T. R. Gruber: A Translation Approach to Portable Ontology Specifications, Knowledge Acquisition, 5(2), 1993.

    Google Scholar 

  18. T. R. Gruber: Toward Principles for the Design of Ontologies Used for Knowledge Sharing, International Journal of Human-Computer Studies (IJHCS), 43(5/6):907–928, 1995.

    Article  Google Scholar 

  19. M. Kifer, G. Lausen, and J. Wu: Logical Foundations of Object-Oriented and Frame-Based Languages, Journal of the ACM, 42, 1995.

    Google Scholar 

  20. G. Klinker, C. Bhola, G. Dallemagne, D. Marques, and J. McDermott: Usable and Reusable Programmin Constructs, Knowledge Acquisition, 3:117–136, 1991.

    Article  Google Scholar 

  21. D. B. Lenat and E. A. Feigenbaum: On the Thresholds of Knowledge. In Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI-87), 1987.

    Google Scholar 

  22. S. Marcus, J. Stout, and J. McDermott VT: An Expert Elevator Designer That Uses Knowledge-based Backtracking, AI Magazine, 9(1):95–111, 1988.

    Google Scholar 

  23. S. Marcus, and J. McDermott: SALT: A Knowledge Acquisition Language for Propose and Revise Systems, Artificial Intelligence, 39(1):1–37.

    Google Scholar 

  24. J. Mc Dermott: Preliminary Steps Toward a Taxonomy of Problem-Solving Methods. In S. Marcus (ed.). Automating Knowledge Acquisition for Experts Systems, Kluwer Academic Publisher, Boston, 1988.

    Google Scholar 

  25. S. Mittal and F. Frayman: Towards a Generic Model of Configuration Tasks. In Proceedings of the 11th International Joint Conference on Artificial Intelligence — IJCAI `89, San Mateo, CA, Morgan-Kaufman, 1989.

    Google Scholar 

  26. R. Mizoguchi, J. Vanwelkenhuysen, and M. Ikeda: Task Ontologies for reuse of Problem Solving Knowledge. In N. J. I. Mars (ed.), Towards Very Large Knowledge Bases, IOS Press, 1995.

    Google Scholar 

  27. E. Motta and Z. Zdrahal: Parametric Design Problem Solving. In Proceedings of the 10th Banff Knowledge Acquisition for Knowledge-Based System Workshop (KAW 96), Banff, Canada, November 9–14,1996.

    Google Scholar 

  28. A. Newell and H. A. Simon: Human Problem Solving, Prentice Hall, 1972.

    Google Scholar 

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

    Google Scholar 

  30. C. Reynaud and F. Tort: Using Explicit Ontologies to Create Problem Solving Methods, International Journal of Human-Computer Studies (IJHCS), 46:339–364, 1997.

    Article  Google Scholar 

  31. A. TH. Schreiber, B. Wielinga, J. M. Akkermans, W. Van De Velde, and R. de Hoog: CommonKADS. A Comprehensive Methodology for KBS Development, IEEE Expert, 9(6):28–37, 1994.

    Article  Google Scholar 

  32. L. Steels: Components of Expertise, AI Magazine, 11(2), 1990.

    Google Scholar 

  33. A. ten Teije: Automated Configuration of Problem Solving Methods in Diagnosis, PhD thesis, University of Amsterdam, Amsterdam, NL, 1997.

    Google Scholar 

  34. P. Terpstra, G. van Heijst, B. Wielinga, and N. Shadbolt: Knowledge Acquisition Support Through Generalised Directive Models. In M. David et al. (eds.): Second Generation Expert Systems, Springer-Verlag, 1993.

    Google Scholar 

  35. J. Top and H. Akkermans: Tasks and Ontologies in Engineering Modeling, International Journal of Human-Computer Studies (IJHCS), 41:585–617, 1994.

    Article  Google Scholar 

  36. G. van Heijst and A. Anjewerden: Four Propositions concerning the specification of Problem-Solving Methods. In Supplementary Proceedings of the 9th European Knowledge Acquisition Workshop EKAW-96, Nottingham, England, May 14–17,1996.

    Google Scholar 

  37. W. van de Velde: Inference Structure as a Basis for Problem Solving. In Proceedings of the 8th European Conference on Artificial Intelligence (ECAI-88), Munich, August 1–5, 1988.

    Google Scholar 

  38. F. van Harmelen and D. Fensel: Formal Methods in Knowledge Engineering, The Knowledge Engineering Review, 10(4), 1995.

    Google Scholar 

  39. G. van Heijst, A. T. Schreiber, and B. J. Wielinga: Using Explicit Ontologies in Knowledge-Based System Development, International Journal of Human-Computer Interaction (IJHCI), to appear 1997.

    Google Scholar 

  40. B. J. Wielinga, J. M. Akkermans, and A. Th. Schreiber: A Formal Analysis of Parametric Design Problem Solving. In Proceedings of the 9th Banff Knowledge Acquisition Workshop (KAW-95), Banff, Canada, January 26–Feruary 3, 1995.

    Google Scholar 

  41. G. R. Yost and T.R. Rothenfluh: Configuring elevator systems, International Journal of Human-Computer Studies (IJHCS), 44(3/4):521–568, 1996.

    Article  Google Scholar 

  42. Z. Zdrahal and E. Motta: An In-Depth Analysis of Propose & Revise Problem Solving' Methods. In Proceedings of the 9th Banff Knowledge Acquisition Workshop (KAW-95), Banff, Canada, January 26–Feruary 3, 1995.

    Google Scholar 

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Enric Plaza Richard Benjamins

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© 1997 Springer-Verlag Berlin Heidelberg

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Fensel, D., Motta, E., Decker, S., Zdrahal, Z. (1997). Using ontologies for defining tasks, problem-solving methods and their mappings. In: Plaza, E., Benjamins, R. (eds) Knowledge Acquisition, Modeling and Management. EKAW 1997. Lecture Notes in Computer Science, vol 1319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026781

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  • DOI: https://doi.org/10.1007/BFb0026781

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