Abstract
The goal of conceptual clustering is to build a set of embedded classes, which cluster objects based on their similarities. Knowledge organization aims at generating the set of most specific classes: the Generalization Space. It has applications in the field of data mining, knowledge indexation or knowledge acquisition. Efficient algorithms have been proposed for data described in éattribute, valueé pairs formalism and for taking into account domain knowledge. Our research focuses on the organization of relational knowledge represented using conceptual graphs. In order to avoid the combinatorial explosion due to the relations in the building of the Generalization Space, we progressively introduce the complexity of the relations. The KIDS algorithm is based upon an iterative data reformulation which allows us to use an efficient propositional knowledge organization algorithm. Experiments show that the KIDS algorithm builds an organization of relational concepts but remains with a complexity that grows linearly with the number of considered objects.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bournaud I., Ganascia J.-G.: Accounting for Domain Knowledge in the Construction of a Generalization Space. ICCS’97, Lectures Notes in AI n°1257, Springer-Verlag (1997) 446–459.
Bournaud I., Zucker J.-D.: Integrating Machine Learning Techniques in a Guided Discovery Tutoring Environment for Chinese Characters. International Journal of Chinese and Oriental Languages Information, Processing Society, 8(2) (1998).
Carpineto C., Romano G.: GALOIS: An order-theoretic approach to conceptual clustering. Tenth International Conference on Machine Learning (1993).
Chein M., Mugnier M.L.: Conceptual Graphs: Fundamental Notions. Revue d’Intelligence Artificielle, 6(4) (1992) 365–406.
Fisher D.: Approaches to conceptual clustering. Ninth International Joint Conference on Artificial Intelligence, Los Angeles, CA, Morgan Kaufmann (1985).
Fisher D.: Knowledge Acquisition Via Incremental Conceptual Clustering. In: Michalski, R.S., Carbonell, J., Mitchell, T.(eds.): Machine Learning: An Artificial Intelligence Approach. San Mateo, CA, Morgan Kaufmann. II (1987) 139–172.
Fisher D.: Iterative Optimization and Simplification of Hierarchical Clusterings. Journal of Artificial Intelligence Research 4 (1996) 147–179.
Gennari J. H., Langley P., Fisher D.: Models of incremental concept formation. Artificial Intelligence 40–1(3) (1989) 11–61.
Ketterlin A., Gancarski P., Korczak J.J.: Conceptual clustering in Structured databases: a Practical Approach. Proceedings of the Knowledge Discovery in Databases KDD’95, AAAI Press (1995).
Kietz J.U. & Morik K.: A polynomial approach to the constructive induction of structural knowledge. Machine Learning 14(2) (1994) 193–217.
Levesque H.J. and Brachman R.J.: A fundamental tradeoff in knowledge representation and reasoning. In: Brachman, R.J, Levesque, H.J. (eds.): Readings in Knowledge Representation. Morgan Kaufmann (1985) 41–70.
Liquiere M., Sallantin J.: Structural Machine Learning with Galois Lattice and Graphs. Fifteen International Conference on Machine Learning (ICML), (1998).
Michalski R. S., Stepp R. E.: An application of AI techniques to structuring objects into an optimal conceptual hierarchy. Seventh International Joint Conference on Artificial Intelligence (1981).
Michalski R. S.: A theory and methodology of inductive learning. Machine Learning: An Artificial Intelligence Approach I, Morgan Kaufmann (1983) 83–129.
Mineau G., Gecsei J., Godin R.: Structuring knowledge bases using Automatic Learning. Sixth International Conference on Data Engineering, Los Angeles, USA (1990).
Muggleton, S., Raedt L. D.: Inductive Logic Programming: Theory and Methods. Journal of Logic Programming 19(20). (1994). 629–679.
Sowa J. F.: Conceptual Structures: Information Processing in Mind and Machine. Addisson-Wesley Publishing Company (1984).
Wnek J., Michalski R.: Hypothesis-driven constructive induction in AQ17-HCI: a method and experiments. Machine Learning 14(2) (1994) 139–168.
Zucker J.-D., Ganascia J.-G.: Changes of Representation for Efficient Learning in Structural Domains. International Conference in Machine Learning, Bari, Italy, Morgan Kaufmann (1996).
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
Bournaud, I., Courtine, M., Zucker, JD. (2000). KIDS: An Iterative Algorithm to Organize Relational Knowledge. In: Dieng, R., Corby, O. (eds) Knowledge Engineering and Knowledge Management Methods, Models, and Tools. EKAW 2000. Lecture Notes in Computer Science(), vol 1937. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39967-4_16
Download citation
DOI: https://doi.org/10.1007/3-540-39967-4_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41119-2
Online ISBN: 978-3-540-39967-4
eBook Packages: Springer Book Archive