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Distance-Based Classification in OWL Ontologies

  • Claudia d’Amato
  • Nicola Fanizzi
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5178)

Abstract

We propose inductive distance-based methods for instance classification and retrieval in ontologies. Casting retrieval as a classification problem with the goal of assessing the individual class-memberships w.r.t. the query concepts, we propose an extension of the k-Nearest Neighbor algorithm for OWL ontologies based on an epistemic distance measure. The procedure can classify the individuals w.r.t. the known concepts but it can also be used to retrieve individuals belonging to query concepts. Experimentally we show that the behavior of the classifier is comparable with the one of a standard reasoner. Moreover we show that new knowledge (not logically derivable) is induced. It can be suggested to the knowledge engineer for validation, during the ontology population task.

Keywords

Description Logic Near Neighbor Training Instance Formal Concept Analysis Closed World Assumption 
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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References

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    Baader, F., Ganter, B., Sertkaya, B., Sattler, U.: Completing description logic knowledge bases using formal concept analysis. In: Veloso, M. (ed.) Proc. of IJCAI (2007)Google Scholar
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    d’Amato, C., Fanizzi, N., Esposito, F.: Reasoning by analogy in description logics through instance-based learning. In: Proc. of SWAP 2006 Workshop, vol. 201. CEUR (2006)Google Scholar
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    Fanizzi, N., d’Amato, C., Esposito, F.: Induction of optimal semi-distances for individuals based on feature sets. In: Working Notes of DL 2007 Workshop, vol. 250. CEUR (2007)Google Scholar
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    Möller, R., Haarslev, V., Wessel, M.: On the scalability of description logic instance retrieval. vol. 189. CEUR (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Claudia d’Amato
    • 1
  • Nicola Fanizzi
    • 1
  • Floriana Esposito
    • 1
  1. 1.Department of Computer ScienceUniversity of Bari 

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