Metric-based stochastic conceptual clustering for ontologies

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Nicola Fanizzi and Claudia d'Amato and 
                 Floriana Esposito",
  title =        "Metric-based stochastic conceptual clustering for
  journal =      "Information Systems",
  year =         "2009",
  volume =       "34",
  pages =        "792--806",
  number =       "8",
  note =         "Sixteenth ACM Conference on Information Knowledge and
                 Management (CIKM 2007)",
  keywords =     "genetic algorithms, genetic programming, Conceptual
  URL =          "",
  DOI =          "doi:10.1016/",
  ISSN =         "0306-4379",
  URL =          "",
  abstract =     "A conceptual clustering framework is presented which
                 can be applied to multi-relational knowledge bases
                 storing resource annotations expressed in the standard
                 languages for the Semantic Web. The framework adopts an
                 effective and language-independent family of
                 semi-distance measures defined for the space of
                 individual resources. These measures are based on a
                 finite number of dimensions corresponding to a
                 committee of discriminating features represented by
                 concept descriptions. The clustering algorithm
                 expresses the possible clusterings in terms of strings
                 of central elements (medoids, w.r.t. the given metric)
                 of variable length. The method performs a stochastic
                 search in the space of possible clusterings, exploiting
                 a technique based on genetic programming. Besides, the
                 number of clusters is not necessarily required as a
                 parameter: a natural number of clusters is autonomously
                 determined, since the search spans a space of strings
                 of different length. An experimentation with real
                 ontologies proves the feasibility of the clustering
                 method and its effectiveness in terms of standard
                 validity indices. The framework is completed by a
                 successive phase, where a newly constructed intensional
                 definition, expressed in the adopted concept language,
                 can be assigned to each cluster. Finally, two possible
                 extensions are proposed. One allows the induction of
                 hierarchies of clusters. The other applies clustering
                 to concept drift and novelty detection in the context
                 of ontologies.",
  notes =        "invited extended version

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