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A Multi-relational Hierarchical Clustering Method for Datalog Knowledge Bases

  • Nicola Fanizzi
  • Claudia d’Amato
  • Floriana Esposito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4994)

Abstract

A clustering method is presented which can be applied to relational knowledge bases (e.g. Datalog deductive databases). It can be used to discover interesting groups of resources through their (semantic) annotations expressed in the standard logic programming languages. The method exploits an effective and language-independent semi-distance measure for individuals., that is based on the resource semantics w.r.t. a number of dimensions corresponding to a committee of features represented by a group of concept descriptions (discriminating features). The algorithm is a fusion of the classic Bisecting k-Means with approaches based on medoids that are typically applied to relational representations. We discuss its complexity and potential applications to several tasks.

Keywords

Dissimilarity Measure Inductive Logic Inductive Logic Programming Concept Description Conceptual Cluster 
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 2008

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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