Abstract
Technical domains are affected by continuous change as a reflection of technological progress. Correspondingly, concept descriptions of terminological knowledge bases for such domains must be adjusted to these dynamics. In this paper we propose a concept versioning methodology which accounts for the evolutionary adaptation of already established concepts according to the gradual shaping of new standards. The approach is based on the provision of a qualitative progress model for the given domain, measures for the prediction and evaluation of the significance of progress, and a representation and update scheme for concept version management.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
S. Carey, Conceptual Change in Childhood, MIT Press, Cambridge/MA, 1985.
P. Thagard, ‘Concepts and conceptual change', Synthese, 82, 255–274, (1990).
J.C. Schlimmer and R.H. Granger, ‘Beyond incremental processing: tracking concept drift', AAAI-86: Proc. 5th National Conf. on Artificial Intelligence, 502–507, Morgan Kaufmann, Los Altos/CA, 1986.
P. Langlcy, ‘A general theory of discrimination learning', In D. Klahr; P. Langley and R. Neches (Eds.), Production System Models of Learning and Development, 99–161, MIT Press, Cambridge/MA, 1987.
P.L. Bartleu, ‘Learning with a slowly changing distribution', COLT'92: Proc. 5th Workshop on Computational Learning Theory, 243–252, 1992.
D.P. Helmbold and P.M. Long, ‘Tracking drifting concepts by minimizing disagreements', Machine Learning, 14 (1), 27–45, (1994).
L. DeRaedt and M. Bruynooghe, ‘Belief updating from integrity constraints and queries', Artificial Intelligence, 53, 192–307, (1992).
U. Hahn, ‘Making understanders out of parsers: semantically driven parsing as a key concept for realistic text understanding applications', International Journal of Intelligent Systems, 4 (3), 345–393, (1989).
R. MacGregor, ‘The evolving technology of classification-based knowledge representation systems', In J. Sowa (Ed.), Principles of Semantic Networks, 385–400, Morgan Kaufmann, San Mateo/CA, 1991.
T.M. Mitchell; R.M. Keller and S.T. Kedar-Cabelli, ‘Explanation-based generalization: a unifying view', Machine Learning, 1 (1), 47–80, (1986).
U. Hahn and M. Klenner, ‘A version tracker for dynamic technical domains as a tool for automated knowledge base evolution', FLAIRS-94: Proc. 7th Florida Art. Intell. Research Symp., 131–136, 1994.
M. Lebowitz, ‘Experiments with incremental concept formation: UNIMEM'. Machine Learning, 2, 103–138 (1987).
J.H. Gennari; P. Langley and D. Fisher, ‘Models of incremental concept formation', Artificial Intelligence, 40, 11–63 (1989).
U. Hahn and M. Klenner, Concept Versioning. CLIF-Report 8/94, AG Linguistische Informatik/Computerlinguistik (CLIF), Universität Freiburg, 1994.
R. MacGregor and R. Bates, The LOOM Knowledge Representation Language. ISI/RS-87-188, ISI, University of Southern California, 1987.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1994 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hahn, U., Klenner, M. (1994). Tracking the evolution of concepts in dynamic worlds. In: Karagiannis, D. (eds) Database and Expert Systems Applications. DEXA 1994. Lecture Notes in Computer Science, vol 856. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58435-8_206
Download citation
DOI: https://doi.org/10.1007/3-540-58435-8_206
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-58435-3
Online ISBN: 978-3-540-48796-8
eBook Packages: Springer Book Archive