Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{DBLP:conf/cikm/FanizzidE07,

• 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 indices.",
• notes = "Replaced by \cite{Fanizzi:2009:IS}",

}

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

Citations