Semantic Web Dog Food

Presented at: 5th European Semantic Web Conference (ESWC2008)

by Nicola Fanizzi, Claudia d'Amato, Floriana Esposito

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

Keywords: concept drift, conceptual clustering, novelty setection, semantic similarity, Machine Learning, Ontology (computer science), Ontology (Computer Science), Semantic Web, Web Ontology Language

Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2008/paper/273
Same as: http://revyu.com/things/eswc-2008-paper-conceptual-clustering-detection
Same as: http://semanticweb.org/id/Conceptual_Clustering_and_its_Application_to_Concept_Drift_and_Novelty_Detection

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