Paper Info
Title
Fuzzy Clustering for Semantic Knowledge Bases
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.
Year
DOI
Venue
2010
10.3233/FI-2010-245
Fundam. Inform.
Keywords
Field
DocType
fuzzy clustering approach,finite number,semantic knowledge bases,major clustering validity index,fuzzy clustering,proposed solution,knowledge base,better cope,semantic web,proposed clustering algorithm,description logics,clustering resource,ontology,clustering
Data mining,Fuzzy clustering,Discrete mathematics,Correlation clustering,Tuple,Theoretical computer science,FLAME clustering,Constrained clustering,Conceptual clustering,Cluster analysis,Mathematics,Medoid
Journal
Volume
Issue
ISSN
99
2
0169-2968
Citations 
PageRank 
References 
3
0.40
22
Authors
3
Name
Order
Citations
PageRank
Floriana Esposito12434277.96
Claudia D'Amato273357.03
Nicola Fanizzi3112490.54