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Lazy Learning from Terminological Knowledge Bases

  • Claudia d’Amato
  • Nicola Fanizzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)

Abstract

This work presents a method founded on instance-based learning algorithms for inductive (memory-based) reasoning on ABoxes. The method, which exploits a semantic dissimilarity measure between concepts and instances, can be employed both to infer class membership of instances and to predict hidden assertions that are not logically entailed from the knowledge base and need to be successively validated by humans (e.g. a knowledge engineer or a domain expert). In the experimentation, we show that the method can effectively help populating an ontology with likely assertions that could not be logically derived.

Keywords

Description Logic Inductive Reasoning Dissimilarity Measure Omission Error Induction Rate 
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 2006

Authors and Affiliations

  • Claudia d’Amato
    • 1
  • Nicola Fanizzi
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di BariBariItaly

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