Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies

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

  author =       "Nicola Fanizzi and Claudia d'Amato and 
                 Floriana Esposito",
  title =        "Evolutionary Clustering in Description Logics:
                 Controlling Concept Formation and Drift in Ontologies",
  booktitle =    "Proceedings of the 19th International Conference,
                 Database and Expert Systems Applications, DEXA 2008",
  year =         "2008",
  editor =       "Sourav S. Bhowmick and Josef K{\"{u}}ng and 
                 Roland R. Wagner",
  series =       "Lecture Notes in Computer Science",
  volume =       "5181",
  pages =        "808--821",
  address =      "Turin, Italy",
  month =        sep # " 1-5",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Cluster
                 Algorithm, Description Logic, Dissimilarity Measure,
                 Concept Drift, Concept Description",
  URL =          "https://doi.org/10.1007/978-3-540-85654-2_73",
  DOI =          "doi:10.1007/978-3-540-85654-2_73",
  timestamp =    "Wed, 24 Jan 2018 12:46:36 +0100",
  biburl =       "https://dblp.org/rec/bib/conf/dexa/FanizzidE08",
  bibsource =    "dblp computer science bibliography, https://dblp.org",
  abstract =     "We present a method based on clustering techniques to
                 detect concept drift or novelty in a knowledge base
                 expressed in Description Logics. The method exploits an
                 effective and language-independent semi-distance
                 measure defined for the space of individuals, that is
                 based on a finite number of dimensions corresponding to
                 a committee of discriminating features (represented by
                 concept descriptions). In the algorithm, the possible
                 clusterings are represented as strings of central
                 elements (medoids, w.r.t. the given metric) of variable
                 length. The number of clusters is not required as a
                 parameter; the method is able to find an optimal choice
                 by means of the evolutionary operators and of a fitness
                 function. An experimentation with some ontologies
                 proves the feasibility of our method and its
                 effectiveness in terms of clustering validity indices.
                 Then, with a supervised learning phase, each cluster
                 can be assigned with a refined or newly constructed
                 intensional definition expressed in the adopted


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