Randomized metric induction and evolutionary conceptual clustering for semantic knowledge bases

We present an evolutionary clustering method which can be applied to multi-relational knowledge bases storing resource annotations expressed in the standard languages for the Semantic Web. The method exploits an effective and languageindependent semi-distance measure defined for the space of individual resources, 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. The clustering algorithm represents the possible clusterings as strings of central elements (medoids, w.r.t. the given metric) of variable length. Hence, the number of clusters is not required as a parameter since the method is able to find an optimal choice by means of the evolutionary operators and of a proper fitness function. We also show how to assign each cluster with a newly constructed intensional de...

• Type: Conference
• Year: 2007
• Where: CIKM
• Authors: Nicola Fanizzi, Claudia d'Amato, Floriana Esposito
• Keywords: CIKM 2007, Clustering Algorithm, Conceptual Clustering, Evolutionary Clustering Method