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Default Clustering with Conceptual Structures

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Book cover Journal on Data Semantics VIII

Part of the book series: Lecture Notes in Computer Science ((JODS,volume 4380))

Abstract

This paper describes a theoretical framework for inducing knowledge from incomplete data sets. The general framework can be used with any formalism based on a lattice structure. It is illustrated within two formalisms: the attribute-value formalism and Sowa’s conceptual graphs. The induction engine is based on a non-supervised algorithm called default clustering which uses the concept of stereotype and the new notion of default subsumption, inspired by the default logic theory. A validation using artificial data sets and an application concerning the extraction of stereotypes from newspaper articles are given at the end of the paper.

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Authors and Affiliations

  1. LIP6, Université Paris VI, 8 rue du Capitaine Scott, 75015 Paris, France

    Julien Velcin & Jean-Gabriel Ganascia

Authors
  1. Julien Velcin
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  2. Jean-Gabriel Ganascia
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Editor information

Stefano Spaccapietra Paolo Atzeni François Fages Mohand-Saïd Hacid Michael Kifer John Mylopoulos Barbara Pernici Pavel Shvaiko Juan Trujillo Ilya Zaihrayeu

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Velcin, J., Ganascia, JG. (2007). Default Clustering with Conceptual Structures. In: Spaccapietra, S., et al. Journal on Data Semantics VIII. Lecture Notes in Computer Science, vol 4380. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70664-9_1

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  • DOI: https://doi.org/10.1007/978-3-540-70664-9_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70663-2

  • Online ISBN: 978-3-540-70664-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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