Abstract
In organism biology, the description of taxa, due to the diversity and the richness of the living world, led to a great accumulation of information which is found again in catalogues and expressed in a language of textual description being various and full of different meanings.
Taking into account these numerous data, we wondered which was the best method for reading out, characterizing and making use of a pertinent knowledge. This article deals with a system of knowledge acquisition, which is an interactive tool using a minimal core of structured descriptions, in order to acquire objects and to discriminate them. This core is progressively extended through the acquisition and recognition of a lot of objects; it represents a generalization hierarchy. The knowledge acquisition, and therefore the building of the generalization hierarchy, is guided by the taxonomy of classes associated to objects. The knowledge acquisition principle is incremental and uses the formalism of conceptual graphs.
We have applied our knowledge acquisition technique to the ichthyological field (ie part of zoology dealing with fishes) as part of a research convention with U.R.2.C (ie research unit on environment and aquatic resources of tropical river valleys) from ORSTOM.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
E. AIMEUR: Acquisition d'Objets Structurés par Caractérisation JAVA: Journées FranÇaises d'Acquisition de Validation et d'Apprentissage, Saint-Raphael, France 1993.
E. AIMEUR, Q. KIEU: A Method of Incremental Acquisition of Structured Objects by Discrimination: Application to Organisms' Biology Knowledge & Data Engineering Workshop, pp 169–182, Strasbourg, France 1993.
N. AUSSENAC: Conception d'une méthodologie et d'un outil d'acquisition de connaissances expertes Thèse de l'université de Paul Sabatier, Toulouse, France 1989.
J. BOOSE: Personal Theory and the Transfer of Human Expertise Proc 3th AAAI, 1984.
J. BOOSE: Expertise Transfer and Complex Problems: using AQUINAS as a Knowledge Acquisition Workbench for Expert Systems Colloque sur l'acquisition des connaissances, Banff, Canada, 1986.
N. CONRUYT, M. MANAGO: Modélisation, Formalisation et Analyse d'Objets biologiques en vue de leur identification 3èmes journées “Symbolique-Numérique”, pp 343–358, Paris, France, 1992
G. ELLIS: Compiled Hierarchical Retrieval Proceedings of the 6th Annual workshop on Conceptual Graphs pp 187–207, Binghamton, USA, 1991.
D.H. FISHER: Knowledge Acquisition Via Conceptual Clustering Machine Learning, vol 2, pp 139–172, 1987.
J.G. GANASCIA: Agape & Charade: deux techniques d'apprentissage symbolique appliquées à la construction de base de connaissances Thèse d'état de l'université de Paris Sud, France, 1987.
O. GEY: COCLUSH: Un Générateur de Classifications d'Objets Structurés, Suivant Différents Points de Vue Actes des 6ièmes Journées FranÇaises de l'apprentissage, pp 165–184, Sete, 1991.
M. LEBOWITZ: Experiments with Incremental Concept Formation: UNIMEM Machine Learning, vol 2, pp 103–138, 1987.
C. LEVEQUE, D. PAUGY, G.G. TEUGELS: Faune des poissons d'eaux douces et saumâtres de l'Afrique de l'Ouest ORSTOM / MRAC Editions, France, 1991.
L. MATILE, P. TASSY, D. GOUJET: Introduction à la systématique zoologique Biosystéma 1. SFS Editions, Paris, 1987.
R.S. MICHALSKI, E. DIDAY, R.E. STEPP: A Recent Advance in Data Analysis: Clustering Objects into Classes Characterized by Conjunctive Concepts Progress in Pattern Recognition, L.N. Kanal & A. Rosenfeld Editions, North-Holland, pp 33–56, 1981.
G. MINEAU, J. GECSEI, R. GODIN: La Classification Symbolique Une Approche Non-Subjective Actes des 5ièmes Journées FranÇaises de l'apprentissage, pp 169–189, Lannion, 1990.
D. PAUGY: Révision Systématique des ALESTES et BRYCINUS Africains Collection Etudes et Thèses, Editions ORSTOM, France, 1986.
J.R. QUINLAN: Induction of Decision Trees Machine Learning 1, pp 81–132, 1986.
J.F. SOWA: Conceptual structures: Information Processing in Mind and Machine Addison-Wesley Publishing Co, 1984.
R.E. STEPP, R.S. MICHALSKI: Learning From Observation: Conceptual Clustering: Inventing Goal Oriented Classifications of Structured Objects Machine Learning: An Artificial Intelligence Approach, vol 2, pp 471–298, Morgan-Kaufman Publishing Co, 1986.
R. VIGNES: Caractérisation automatique de groupes biologiques Thèse de l'université de Paris 6, France, 1991.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aïmeur, E., Ganascia, J.G. (1993). Elicitation of taxonomies based on the use of conceptual graph operators. In: Mineau, G.W., Moulin, B., Sowa, J.F. (eds) Conceptual Graphs for Knowledge Representation. ICCS 1993. Lecture Notes in Computer Science, vol 699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56979-0_20
Download citation
DOI: https://doi.org/10.1007/3-540-56979-0_20
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-56979-4
Online ISBN: 978-3-540-47848-5
eBook Packages: Springer Book Archive