Abstract
When knowledge systems are deployed into a real-world application, then the maintenance of the knowledge is a crucial success factor. In the past, some approaches for the automatic refinement of knowledge bases have been proposed. Many only provide limited control during the modification and refinement process, and often assumptions about the correctness of the knowledge base and case base are made. However, such assumptions do not necessarily hold for real-world applications.
In this paper, we present a novel interactive approach for the user-guided refinement of knowledge bases. Subgroup mining methods are used to discover local patterns that describe factors potentially causing incorrect behavior of the knowledge system. We provide a case study of the presented approach with a fielded system in the medical domain.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ginsberg, A.: Automatic Refinement of Expert System Knowledge Bases. Morgan Kaufmann, San Francisco (1988)
Carbonara, L., Sleeman, D.: Effective and Efficient Knowledge Base Refinement. Machine Learning 37, 143–181 (1999)
Boswell, R., Craw, S.: Organizing Knowledge Refinement Operators. In: Validation and Verification of Knowledge Based Systems, pp. 149–161. Kluwer, Oslo (1999)
Knauf, R., Philippow, I., Jantke, K.P., Gonzalez, A., Salecker, D.: System Refinement in Practice – Using a Formal Method to Modify Real-Life Knowledge. In: Proceedings of the 15th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2002). AAAI Press, Menlo Park (2002)
Wrobel, S.: An Algorithm for Multi-Relational Discovery of Subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)
Klösgen, W.: Subgroup Discovery. In: Handbook of Data Mining and Knowledge Discovery, ch. 16.3. Oxford University Press, New York (2002)
Gamberger, D., Lavrac, N., Krstacic, G.: Active Subgroup Mining: a Case Study in Coronary Heart Disease Risk Group Detection. Artificial Intelligence in Medicine 28, 27–57 (2003)
Gamberger, D., Lavrac, N.: Expert-Guided Subgroup Discovery: Methodology and Application. Journal of Artificial Intelligence Research 17, 501–527 (2002)
Puppe, F.: Knowledge Reuse among Diagnostic Problem-Solving Methods in the Shell-Kit D3. Intl. Journal of Human-Computer Studies 49, 627–649 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Atzmueller, M., Baumeister, J., Hemsing, A., Richter, EJ., Puppe, F. (2005). Subgroup Mining for Interactive Knowledge Refinement. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_61
Download citation
DOI: https://doi.org/10.1007/11527770_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
eBook Packages: Computer ScienceComputer Science (R0)