Abstract
In order to build descriptions of prototypical situations, we first developed a system, MLK (Memorization for Learning Knowledge), allowing us to gather events related to similar situations starting from descriptions found in texts, these events being represented by conceptual graphs. One of the stages to build these prototypes consists of generalizing some similar graphs in order to produce a description. In this paper, we present a cost bounded algorithm of conceptual graph generalization, proceeding by ascending clustering. The use of costs on the operations of generalization allows us to control the growth of the search space.
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
Mooney, R., DeJong, G.: Learning schemata for natural language processing. In: Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles (1985)
Pazzani, M.J.: Integrating explanation-based and empirical learning methods in OCCAM. In: Proceedings of the Third European Working Session on Learning, Glasgow (1988)
Ram, A.: Indexing, elaboration and refinement: incremental learning of explanatory cases. Machine Learning 10(3), 7–54 (1993)
Ferret, O., Grau, B.: An Aggregation Procedure for Building Episodic Memory. In: 15th IJCAI, Nagoya (1997)
Ferret, O., Grau, B.: Construire une mémoire épisodique à partir de textes: pourquoi et comment? RIA 12(3), 317–409 (1998)
Sowa, J.F.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)
Mineau, G.W.: Induction on Conceptual Graphs: Finding Common Generalizations and Compatible Projections. In: Nagle, T.E., Nagle, J.A., Gerholz, L.L., Eklund, P.W. (eds.) Conceptual Structures - current research and practice. Ellis Horwood, New York (1992)
Bournaud, I.: Construction de hiérarchies conceptuelles pour l’organisation de connaissances. In: LMO 1996 (1996)
Ellis, G.: Compiled Hierarchical Retrieval. In: Nagle, T.E., Nagle, J.A., Gerholz, L.L., Eklund, P.W. (eds.) Conceptual Structures - current research and practice. Ellis Horwood, New York (1992)
Michalski, K.B., Stepp, R.E.: Learning from observation: Conceptual clustering. Machine Learning, An Artificial Approach 1, 331–363 (1983)
Champesme, M.: Using Empirical Subsumption to Reduce Search Space in Learning. In: International Conference on Conceptual Structures, ICCS 1995, Santa Cruz, California, USA (1995)
Chibout, K., Vilnat, A.: Computational Processing of Verbal Polysemy with Conceptual Structures. In: Mugnier, M.-L., Chein, M. (eds.) ICCS 1998. LNCS (LNAI), vol. 1453, pp. 367–374. Springer, Heidelberg (1998)
Muggleton, S., De Raedt, L.: Inductive Logic Programming: Theory and Methods. Journal of Logic Programming 19(20), 629–679 (1994)
Erdem, E., Flener, P.: A Re-Definition of Least Generalizations, and Construction Modes as a New Declarative Bias for ILP (1998) (Unpublished), http://www.cs.utexas.edu/users/esra/papers/ErdFle98.ps
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
de Chalendar, G., Grau, B., Ferret, O. (2000). A Cost-Bounded Algorithm to Control Events Generalization. In: Ganter, B., Mineau, G.W. (eds) Conceptual Structures: Logical, Linguistic, and Computational Issues. ICCS 2000. Lecture Notes in Computer Science(), vol 1867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10722280_38
Download citation
DOI: https://doi.org/10.1007/10722280_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67859-5
Online ISBN: 978-3-540-44663-7
eBook Packages: Springer Book Archive