Our goal is to model the way people induce knowledge from rare and sparse data. This paper describes a theoretical framework for inducing knowledge from these incomplete data described with conceptual graphs. The induction engine is based on a non-supervised algorithm named default clustering which uses the concept of stereotype and the new notion of default subsumption, the latter being inspired by the default logic theory. A validation using artificial data sets and an application concerning an historic case are given at the end of the paper. © Springer-Verlag 2004.
CITATION STYLE
Velcin, J., & Ganascia, J. G. (2004). Modeling default induction with conceptual structures. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3288, 83–95. https://doi.org/10.1007/978-3-540-30464-7_8
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