Metric-based stochastic conceptual clustering for ontologies

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@Article{Fanizzi:2009:IS,

• author = "Nicola Fanizzi and Claudia d'Amato and Floriana Esposito",
• title = "Metric-based stochastic conceptual clustering for ontologies",
• journal = "Information Systems",
• year = "2009",
• volume = "34",
• pages = "792--806",
• number = "8",
• note = "Sixteenth ACM Conference on Information Knowledge and Management (CIKM 2007)",
• keywords = "genetic algorithms, genetic programming, Conceptual clustering",
• URL = "https://dblp.uni-trier.de/rec/bibtex/journals/is/FanizzidE09",
• DOI = "doi:10.1016/j.is.2009.03.008",
• ISSN = "0306-4379",
• URL = "http://www.sciencedirect.com/science/article/B6V0G-4W3HXC0-1/2/95a1535c9097d816c4ec5ad804772c4b",
• abstract = "A conceptual clustering framework is presented which can be applied to multi-relational knowledge bases storing resource annotations expressed in the standard languages for the Semantic Web. The framework adopts an effective and language-independent family of semi-distance measures defined for the space of individual resources. These measures are based on a finite number of dimensions corresponding to a committee of discriminating features represented by concept descriptions. The clustering algorithm expresses the possible clusterings in terms of strings of central elements (medoids, w.r.t. the given metric) of variable length. The method performs a stochastic search in the space of possible clusterings, exploiting a technique based on genetic programming. Besides, the number of clusters is not necessarily required as a parameter: a natural number of clusters is autonomously determined, since the search spans a space of strings of different length. An experimentation with real ontologies proves the feasibility of the clustering method and its effectiveness in terms of standard validity indices. The framework is completed by a successive phase, where a newly constructed intensional definition, expressed in the adopted concept language, can be assigned to each cluster. Finally, two possible extensions are proposed. One allows the induction of hierarchies of clusters. The other applies clustering to concept drift and novelty detection in the context of ontologies.",
• notes = "invited extended version \cite{DBLP:conf/cikm/FanizzidE07}",

}

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

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