Transductive Inference for Class-Membership Propagation in Web Ontologies

  • Pasquale Minervini
  • Claudia d’Amato
  • Nicola Fanizzi
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7882)

Abstract

The increasing availability of structured machine-processable knowledge in the context of the Semantic Web, allows for inductive methods to back and complement purely deductive reasoning in tasks where the latter may fall short. This work proposes a new method for similarity-based class-membership prediction in this context. The underlying idea is the propagation of class-membership information among similar individuals. The resulting method is essentially non-parametric and it is characterized by interesting complexity properties, that make it a candidate for the application of transductive inference to large-scale contexts. We also show an empirical evaluation of the method with respect to other approaches based on inductive inference in the related literature.

Keywords

Description Logic Inductive Inference Training Individual Graph Regularization 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 2013

Authors and Affiliations

  • Pasquale Minervini
    • 1
  • Claudia d’Amato
    • 1
  • Nicola Fanizzi
    • 1
  • Floriana Esposito
    • 1
  1. 1.LACAM, Dipartimento di InformaticaUniversità degli Studi di Bari “Aldo Moro”BariItalia

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