Clustering Individuals in Ontologies: a Distance-based Evolutionary Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Fanizzi:2007:MCD,

• 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 = "http://www.ecmlpkdd2007.org/CD/workshops/MCDM/18_Fanizzi/mcdws2007-final.pdf",
• 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

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