Sciweavers

Share
DEXA
2008
Springer

Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies

11 years 10 months ago
Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies
Abstract. We present a method based on clustering techniques to detect concept drift or novelty in a knowledge base expressed in Description Logics. The method exploits an effective and language-independent semi-distance measure defined for the space of individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (represented by concept descriptions). In the algorithm, the possible clusterings are represented as strings of central elements (medoids, w.r.t. the given metric) of variable length. The number of clusters is not required as a parameter; the method is able to find an optimal choice by means of the evolutionary operators and of a fitness function. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices. Then, with a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in...
Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where DEXA
Authors Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
Comments (0)
books