by F Esposito, Claudia D'Amato, Nicola Fanizzi
Abstract:
This work focusses on the problem of clustering resources contained in knowledge bases represented throughmulti-relational standard languages that are typical for the context of the Semantic Web, and ultimately founded in Description Logics. The proposed solution relies on effective and language-independent dissimilarity measures that are based on a finite number of dimensions corresponding to a committee of discriminating features, that stands for a context, represented by concept descriptions in Description Logics. The proposed clustering algorithm expresses the possible clusterings in tuples of central elements: in this categorical setting, we resort to the notion of medoid, w.r.t. the given metric. These centers are iteratively adjusted following the rationale of fuzzy clustering approach, i.e. one where the membership to each cluster is not deterministic but graded, ranging in the unit interval. This better copes with the inherent uncertainty of the knowledge bases expressed in Description Logics which adopt an open-world semantics. An extensive experimentation with a number of ontologies proves the feasibility of our method and its effectiveness in terms of major clustering validity indices.
Reference:
Fuzzy Clustering for Semantic Knowledge Bases (F Esposito, Claudia D'Amato, Nicola Fanizzi), In Fundamenta Informaticae, IOS Press, volume 99, 2010.
Bibtex Entry:
@article{esposito2010fuzzy,
abstract = {This work focusses on the problem of clustering resources contained in knowledge bases represented throughmulti-relational standard languages that are typical for the context of the Semantic Web, and ultimately founded in Description Logics. The proposed solution relies on effective and language-independent dissimilarity measures that are based on a finite number of dimensions corresponding to a committee of discriminating features, that stands for a context, represented by concept descriptions in Description Logics. The proposed clustering algorithm expresses the possible clusterings in tuples of central elements: in this categorical setting, we resort to the notion of medoid, w.r.t. the given metric. These centers are iteratively adjusted following the rationale of fuzzy clustering approach, i.e. one where the membership to each cluster is not deterministic but graded, ranging in the unit interval. This better copes with the inherent uncertainty of the knowledge bases expressed in Description Logics which adopt an open-world semantics. An extensive experimentation with a number of ontologies proves the feasibility of our method and its effectiveness in terms of major clustering validity indices.},
author = {Esposito, F and D'Amato, Claudia and Fanizzi, Nicola},
journal = {Fundamenta Informaticae},
keywords = {SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
number = {2},
pages = {187--205},
publisher = {IOS Press},
title = {{Fuzzy Clustering for Semantic Knowledge Bases}},
volume = {99},
year = {2010}
}