Modeling default induction with conceptual structures

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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Conference papers
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https://hal.archives-ouvertes.fr/hal-01520642
Contributor : Lip6 Publications <>
Submitted on : Wednesday, May 10, 2017 - 4:47:59 PM
Last modification on : Thursday, March 21, 2019 - 2:32:43 PM

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Julien Velcin, Jean-Gabriel Ganascia. Modeling default induction with conceptual structures. International Conference on Conceptual Modeling (ER), Nov 2004, Shangai, China. pp.83-95, ⟨10.1007/978-3-540-30464-7_8⟩. ⟨hal-01520642⟩

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