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ReduCE: A Reduced Coulomb Energy Network Method for Approximate Classification

  • Nicola Fanizzi
  • Claudia d’Amato
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5554)

Abstract

In order to overcome the limitations of purely deductive approaches to the tasks of classification and retrieval from ontologies, inductive (instance-based) methods have been proposed as efficient and noise-tolerant alternative. In this paper we propose an original method based on non-parametric learning: the Reduced Coulomb Energy (RCE) Network. The method requires a limited training effort but it turns out to be very effective during the classification phase. Casting retrieval as the problem of assessing the class-membership of individuals w.r.t. the query concepts, we propose an extension of a classification algorithm using RCE networks based on an entropic similarity measure for OWL. Experimentally we show that the performance of the resulting inductive classifier is comparable with the one of a standard reasoner and often more efficient than with other inductive approaches. Moreover, we show that new knowledge (not logically derivable) is induced and the likelihood of the answers may be provided.

Keywords

Description Logic Training Instance Inductive Procedure Query Answering Semantic Similarity Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Nicola Fanizzi
    • 1
  • Claudia d’Amato
    • 1
  • Floriana Esposito
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli studi di BariBariItaly

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