Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Nicola Fanizzi and Claudia d'Amato and 
                 Floriana Esposito",
  title =        "Randomized metric induction and evolutionary
                 conceptual clustering for semantic knowledge bases",
  booktitle =    "Proceedings of the Sixteenth {ACM} Conference on
                 Information and Knowledge Management, CIKM 2007",
  year =         "2007",
  editor =       "M{\'{a}}rio J. Silva and Alberto H. F. Laender and 
                 Ricardo A. Baeza{-}Yates and Deborah L. McGuinness and 
                 Bj{\o}rn Olstad and {\O}ystein Haug Olsen and 
                 Andr{\'{e}} O. Falc{\~{a}}o",
  pages =        "51--60",
  address =      "Lisbon, Portugal",
  month =        nov # " 6-10",
  publisher =    "{ACM}",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-59593-803-9",
  URL =          "http://doi.acm.org/10.1145/1321440.1321450",
  timestamp =    "Fri, 02 Jun 2017 20:47:30 +0200",
  biburl =       "https://dblp.org/rec/bib/conf/cikm/FanizzidE07",
  bibsource =    "dblp computer science bibliography, https://dblp.org",
  DOI =          "doi:10.1145/1321440.1321450",
  abstract =     "We present an evolutionary clustering method which can
                 be applied to multi-relational knowledge bases storing
                 semantic resource annotations expressed in the standard
                 languages for the Semantic Web. The method exploits an
                 effective and language-independent semi-distance
                 measure defined for the space of individual resources,
                 that is based on a finite number of dimensions
                 corresponding to a committee of features represented by
                 a group of concept descriptions (discriminating
                 features). We show how to obtain a maximally
                 discriminating group of features through a feature
                 construction method based on genetic programming. The
                 algorithm represents the possible clusterings as
                 strings of central elements (medoids, w.r.t. the given
                 metric) of variable length. Hence, the number of
                 clusters is not needed as a parameter since the method
                 can optimize it by means of the mutation operators and
                 of a proper fitness function. We also show how to
                 assign each cluster with a newly constructed
                 intensional definition in the employed concept
                 language. An experimentation with some ontologies
                 proves the feasibility of our method and its
                 effectiveness in terms of clustering validity
  notes =        "Replaced by \cite{Fanizzi:2009:IS}",

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