Evolutionary Conceptual Clustering of Semantically Annotated Resources

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Fanizzi:2007:ICSC,
  author =       "Nicola Fanizzi and Claudia d'Amato and 
                 Floriana Esposito",
  booktitle =    "International Conference on Semantic Computing (ICSC
                 2007)",
  title =        "Evolutionary Conceptual Clustering of Semantically
                 Annotated Resources",
  year =         "2007",
  pages =        "783--790",
  address =      "Irvine, CA, USA",
  month =        "17-19 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICSC.2007.92",
  abstract =     "A clustering method is presented which can be applied
                 to knowledge bases storing semantically annotated
                 resources. The method can be used to discover groupings
                 of structured objects expressed in the standard concept
                 languages employed in the Semantic Web. The method
                 exploits effective language-independent semi-distance
                 measures over the space of resources. These are based
                 on their semantics w.r.t. a number of dimensions
                 corresponding to a committee of features represented by
                 a group of discriminating concept descriptions. We show
                 how to obtain a maximally discriminating group of
                 features through a feature construction procedure 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 an optimal set of clusters by means of a proper
                 fitness function based on the defined distance and the
                 discernibility criterion. An experimentation with some
                 real ontologies proves the feasibility of our method.",
  notes =        "also known as \cite{4338423}",
}

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

Citations