Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases
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gp-bibliography.bib Revision:1.8081
- @InProceedings{DBLP:conf/cikm/FanizzidE07,
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author = "Nicola Fanizzi and Claudia d'Amato and
Floriana Esposito",
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title = "Randomized metric induction and evolutionary
conceptual clustering for semantic knowledge bases",
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booktitle = "Proceedings of the Sixteenth {ACM} Conference on
Information and Knowledge Management, CIKM 2007",
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year = "2007",
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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",
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pages = "51--60",
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address = "Lisbon, Portugal",
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month = nov # " 6-10",
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publisher = "{ACM}",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-59593-803-9",
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URL = "http://doi.acm.org/10.1145/1321440.1321450",
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timestamp = "Fri, 02 Jun 2017 20:47:30 +0200",
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biburl = "https://dblp.org/rec/bib/conf/cikm/FanizzidE07",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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DOI = "doi:10.1145/1321440.1321450",
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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.",
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notes = "Replaced by \cite{Fanizzi:2009:IS}",
- }
Genetic Programming entries for
Nicola Fanizzi
Claudia d'Amato
Floriana Esposito
Citations