Abstract
Validation is a critical process in the whole knowledge-based system life cycle. A knowledge base incorporated into such systems has to be verified or (more generally) validated. There have been many approaches to develop specialised procedures and techniques, aimed at assuring the highest level of knowledge quality. Keeping in mind “knowledge validation mappings”, we believe a more global view is necessary to facilitate applying the proper techniques, so the paper deals with practical guidelines of knowledge validation (KV). Facing the most popular techniques of KV: decision tables-based, decision trees-based or nets-based with the criteria set to be utilised, we try to define certain principles useful in the validation procedures referring to two levels: general and detailed. The first one refers to paradigms, which arise from interrelationships among the crucial components of the KV process (procedures, approaches and criteria). The detailed principles are addressed to specific forms used for knowledge representations: rules, frames, neural nets and others.
The golden rule is that there are no golden rules. (G.B. Shaw)
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bonner R.: Economics of Information and Acquisition of Knowledge. AE, Wroclaw. Prace Naukowe AE [Research Papers of the AE] no. 787, 1998.
Coenen F.P, Dunne P.E.: Verification and Validation of Rulebases using Binary Encoded Incidence Matrix Technique. EUROVAV-97, 4th European Symposium on the Validation and Verification of Knowledge Based Systems, Leuven, 1997
Cragun B. J., Steudel H. J.: A decision-table-based processor for checking completeness and consistency in rule-based expert systems. Man-Machine Studies, No. 26, 1987.
Fagin R, Halpern J.Y, Moses Y., Vardi M.Y.: Reasoning about Knowledge. The MIT Press, Cambridge, 1995.
Horgan J.: The End of Science. Facing the Limits of Knowledge in the Twilight of the Scientific Age. Addison-Wesley Publ. Co., 1996
Jakubczyc J.A., Owoc M.L.: Knowledge Management and Artificial Intelligence. Argumenta OeconomicA, no. 1 (6), Wroclaw University of Economics, 1998.
Laurent J.P.: Proposals for a Valid Terminology in KBS Validation. ECAI 92. 10th European Conference on Artificial Intelligence. John Wiley & Sons, Ltd., 1992.
Liu N.K., Dillon T.: An Approach Towards the Verification of Expert Systems Using Numerical Petri Nets. International Journal of Intelligent Systems, Vol. 6, 1991.
Lounis H.: Knowledge-Based Systems Verification: A Machine Learning-Based Approach. Expert Systems With Applications, Val. 8, No. 3, 1995.
Nazareth D.L., Kennedy M. H.: Verification of Rule-Based Knowledge using Directed Graphs. Knowledge Acquisition, 1991.
AE, Wroclaw. Prace Naukowe AE [Research Papers of the AE] no. 787, 1998 (in Polish)
O’Leary D.E.: A Probability of Fuzzy Events Approach to Validating Expert Systems in a Multiple Agent Environment. Expert Systems With Applications, Vol. 7, No. 2, 1994
Owoc M.L.: From Local to Global Validation of a Knowledge Base. AE, Wroclaw, Prace Naukowe AE [Research Papers of the AE] No. 772, 1998
Owoc M.L. Measuring Aspects of Knowledge Validation. AE, Wroclaw. Prace Naukowe AE [Research Papers of the AE] no. 787, 1998
Owoc M.L., Ochmanska M.: Limits of Knowledge Base Validation. EXPERSYS-96. Artificial Intelligence Applications. IITT — International Paris, 1996
Owoc, M. L., Galant V.: Validation of Rule-Based Systems Generated by Classification Algorithms, in: Proceedings of the Information Systems Development Conference, Bled’98 — Slovenia, Kluwer Academic/Plenum Pub., 1999
Owoc M.L., Ochmanska M.: Towards Knowledge Validation Theory. AE, Wroclaw, Prace Naukowe AE [Research Papers of the AE] no. 815, 1999
Schultz R.D., Geissman J.R.: Bridging the Gap Between Static and Dynamic Verification. Proc. of AAAI-88 Workshop on Validation and Verification Expert Systems. AAA’, 1988
Suh Y., Murray T. J.: A Tree-Based Approach for Verifying Completeness and Consistency in Rule-Based Systems. Expert Systems With Applications, Vol. 7, No. 2, 1994.
Talavera L., Cortes U.: Inductive hypothesis validation and bias selection in unsupervised learning. EUROVAV-97, 4th European Symposium on the Validation and Verification of Knowledge Based Systems, Leuven, 1997
Vanthienen J. Dries E.: Illustration of a Decision Table Tool for specifying and implementing Knowledge Based Systems. Proc. of the Fifth International Conf. On Tools with A.I., 1993
Zlatarewa N., Preece A.: State of the Art in Automated Validation of Knowledge-Based Systems. Expert Systems with Applications Vol. 7, No. 2, 1994
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer Science+Business Media New York
About this chapter
Cite this chapter
Owoc, M.L., Ochmanska, M., Gladysz, T. (1999). On Principles of Knowledge Validation. In: Vermesan, A., Coenen, F. (eds) Validation and Verification of Knowledge Based Systems. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6916-6_2
Download citation
DOI: https://doi.org/10.1007/978-1-4757-6916-6_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-5107-6
Online ISBN: 978-1-4757-6916-6
eBook Packages: Springer Book Archive