Abstract
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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Velcin, J., Ganascia, JG. (2004). Modeling Default Induction with Conceptual Structures. In: Atzeni, P., Chu, W., Lu, H., Zhou, S., Ling, TW. (eds) Conceptual Modeling – ER 2004. ER 2004. Lecture Notes in Computer Science, vol 3288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30464-7_8
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DOI: https://doi.org/10.1007/978-3-540-30464-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23723-5
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