Clustering Individuals in Ontologies: a Distance-based Evolutionary Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Nicola Fanizzi and Claudia d'Amato and 
                 Floriana Esposito",
  title =        "Clustering Individuals in Ontologies: a Distance-based
                 Evolutionary Approach",
  booktitle =    "Proceedings of the third ECML/PKDD international
                 workshop on Mining Complex Data",
  year =         "2007",
  editor =       "Zbigniew W. Ras and Djamel Zighed and 
                 Shusaku Tsumoto",
  pages =        "197--208",
  address =      "Warsaw",
  month =        "17 and 21 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  size =         "12 pages",
  abstract =     "A clustering method is presented which can be applied
                 to semantically annotated resources in the context of
                 ontological knowledge bases. This method can be used to
                 discover interesting groupings of structured objects
                 through expressed in the standard languages employed
                 for modeling concepts in the Semantic Web. The method
                 exploits an effective and language-independent
                 semidistance measure over the space of resources, that
                 is based on their semantics w.r.t. a number of
                 dimensions corresponding to a committee of features
                 represented by a group of concept descriptions
                 (discriminating features). A maximally discriminating
                 group of features can be constructed through a feature
                 construction method based on genetic programming. The
                 evolutionary clustering algorithm employed is based on
                 the notion of medoids applied to relational
                 representations. It is able to induce a set of clusters
                 by means of a proper fitness function based on a
                 discernibility criterion. An experimentation with some
                 ontologies proves the feasibility of our method.",
  notes =        "LACAM Dipartimento di Informatica, Universit`a degli
                 Studi di Bari Campus Universitario, Via Orabona 4 70125
                 Bari, Italy",

Genetic Programming entries for Nicola Fanizzi Claudia d'Amato Floriana Esposito