RULES AS SIMPLE WAY TO MODEL KNOWLEDGE - Closing the Gap between Promise and Reality

Valentin Zacharias

2008

Abstract

There is a considerable gap between the potential of rules bases to be a simpler way to formulate high level knowledge and the reality of tiresome and error prone rule bases creation processes. Based on the experience from three rule base creation projects this paper identifies reasons for this gap between promise and reality and proposes steps that can be taken to close it. An architecture for a complete support of rule base development is presented.

References

  1. Baroff, J., Simon, R., Gilman, F., and Shneiderman, B. (1987). Direct manipulation user interfaces for expert systems. pages 99-125.
  2. Brna, P., Brayshaw, M., Esom-Cook, M., Fung, P., Bundy, A., and Dodd, T. (1991). An overview of prolog debugging tools. Instructional Science, 20(2):193-214.
  3. Chalupsky, H. and Russ, T. (2002). Whynot: Debugging failed queries in large knowledge bases. In Proceedings of the Fourteenth Innovative Applications of Artificial Intelligence Conference (IAAI-02), pages 870- 877.
  4. Craw, S. and Boswell, R. (2000). Debugging knowledgebased applications with a generic toolkit. In ICTAI, pages 182-185.
  5. Craw, S. and Sleeman, D. (1996). Knowledge based refinement of knowledge based systems. Technical report, The Robert Gordon University.
  6. Decker, S., Erdmann, M., Fensel, D., and Studer, R. (1999). Ontobroker: Ontology-based access to distributed and semi-structured unformation. In Database Semantics: Semantic Issues in Multimedia Systems, pages 351- 369.
  7. DeMillo, R. A., Lipton, R. J., and Sayward, F. G. (1978). Hints on test data selection: Help for the practicing programmer. IEEE Computer, 11(4):34-41.
  8. Dershowitz, N. and Lee, Y.-J. (1987). Deductive debugging. In SLP, pages 298-306.
  9. Ginsberg, A. (1988). Automatic Refinement of Expert System Knowledge Bases. Morgan Kaufmann Publishers.
  10. Grossner, C., Gokulchander, P., Preece, A., and Radhakrishnan, T. (1994). Revealing the structure of rule based systems. International Journal of Expert Systems.
  11. Gupta, U. (1993). Validation and verification of knowledgebased systems: a survey. Journal of Applied Intelligence, pages 343-363.
  12. Jacob, R. and Froscher, J. (1990). A software engineering methodology for rule-based systems. IEEE Transactions on Knowledge and Data Engineering, 2(2):173- 189.
  13. Kifer, M., de Bruijn, J., Boley, H., and Fensel, D. (2005). A realistic architecture for the semantic web. In RuleML, pages 17-29.
  14. Kifer, M., Lausen, G., and Wu, J. (1995). Logical foundations of object-oriented and frame-based languages. Journal of the ACM, 42(4):741-843.
  15. Martincic, C. J. (2001). Mechanisms for answering ”why not” questions in rule- and object-based systems. PhD thesis, University of Pittsburgh. Adviser-Douglas P. Metzler.
  16. McCarthy, J. (1959). Programs with common sense. In Proceedings of the Teddington Conference on the Mechanization of Thought Processes, pages 75-91, London. Her Majesty's Stationary Office.
  17. Mehrotra, M. (1991). ”rule groupings: a software engineering approach towards verification of expert systems”. Technical report, NASA Contract NAS1-18585, Final Rep.
  18. Morik, J.-U. K. K. and Emde, W. (1993). Knowledge Acquisition and Machine Learning. Academic Press, London.
  19. Naish, L. (1992). Declarative diagnosis of missing answers. New Generation Comput., 10(3):255-286.
  20. Ourston, D. and Mooney, R. (1990). Changing the rules: A comprehensive approach to theory refinement. In Proceedings of the Eighth National Conference on Artificial Intelligence, pages 815-520.
  21. Pereira, L. M. (1986). Rational debugging in logic programming. In Proceedings of the Third International Conference on Logic Programming, pages 203-210.
  22. Preece, A. D. and Shinghal, R. (1994). Foundation and application of knowledge base verification. International Journal of Intelligent Systems, 9(8):683-701.
  23. Preece, A. D., Talbot, S., and Vignollet, L. (1997). Evaluation of verification tools for knowledge-based systems. Int. J. Hum.-Comput. Stud., 47(5):629-658.
  24. Raedt, L. D. (1992). Interactive Theory Revision. Academic Press, London.
  25. Richards, R. M. B. (1991). First-order theory revision. In Machine Learning: Proceedings of the Eighth International Workshop on Machine Learning, pages 447- 451.
  26. Ruthruff, J. and Burnett, M. (2005). Six challenges in supporting end-user debugging. In 1st Workshop on EndUser Software Engineering (WEUSE 2005) at ICSE 05.
  27. Ruthruff, J., Phalgune, A., Beckwith, L., and Burnett, M. (2004). Rewarding good behavior: End-user debugging and rewards. In VL/HCC'04: IEEE Symposium on Visual Languages and Human-Centric Computing.
  28. Saitta, L., Botta, M., and Neri, F. (1993). Multistrategy learning and theory revision. Machine Learning, 11(2):153-172.
  29. Shapiro, E. Y. (1982). Algorithmic program debugging. PhD thesis, Yale University.
  30. Stumptner, M. and Wotawa, F. (1998). A survey of intelligent debugging. AI Commun., 11(1):35-51.
  31. Tsai, W.-T., Vishnuvajjala, R., and Zhang, D. (1999). Verification and validation of knowledge-based systems. IEEE Transactions on Knowledge and Data Engineering, 11(1):202-212.
  32. Waterman, D. A. (1968). Machine Learning of Heuristics. PhD thesis, Stanford University.
  33. Wilkins, D. (1990). Knowledge base refinement as improving and incorrect, inconsistent and incomplete domain theory. Machine Learning, 3:493-513.
  34. Wogulis, J. and Pazzani, M. (1993). A methodology for evaluating theory revision systems: results with audrey ii. In Proceedings of the Sixth International Workshop on Machine Learning, pages 332-337.
  35. Zacharias, V. (2007). Visualization of rule bases - the overall structure. In 7th International Conference on Knowledge Management - Special Track on Knowledge Visualization and Knowledge Discovery.
  36. Zacharias, V. and Abecker, A. (2007). Explorative debugging for rapid rule base development. In Proceedings of the 3rd Workshop on Scripting for the Semantic Web at the ESWC 2007.
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Paper Citation


in Harvard Style

Zacharias V. (2008). RULES AS SIMPLE WAY TO MODEL KNOWLEDGE - Closing the Gap between Promise and Reality . In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8111-37-1, pages 87-94. DOI: 10.5220/0001712500870094


in Bibtex Style

@conference{iceis08,
author={Valentin Zacharias},
title={RULES AS SIMPLE WAY TO MODEL KNOWLEDGE - Closing the Gap between Promise and Reality},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2008},
pages={87-94},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001712500870094},
isbn={978-989-8111-37-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - RULES AS SIMPLE WAY TO MODEL KNOWLEDGE - Closing the Gap between Promise and Reality
SN - 978-989-8111-37-1
AU - Zacharias V.
PY - 2008
SP - 87
EP - 94
DO - 10.5220/0001712500870094