Conceptual Clustering and its Application to Concept Drift and Novelty Detection

A paper written by Claudia d'Amato, Nicola Fanizzi and Floriana Esposito. It was presented at the ESWC2008. It is about Concept drift, Novelty setection, Conceptual clustering and semantic similarity

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[edit] Abstract

We present a method based on clustering techniques to detect concept drift or novelty in a knowledge based 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). A maximally discriminating group of features can be obtained with the randomized optimization methods described in the paper. 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 the adopted language. We propose a method for exploiting the clustering results for concept drift and novelty detection

This data has been imported from the ESWC2008 data