Paper 3

Dimensional Clustering of Linked Data: Techniques and Applications

Authors: Alo Ferrara, Lorenzo Genta, Stefano Montanelli, and Silvana Castano

Volume 19 (2015)

Abstract

The plurality and heterogeneity of linked data features require appropriate solutions for accurate matching and clustering. In this paper, we propose a dimensional clustering approach to enforce i) the capability to select the set of features to use for data matching and clustering, that are packaged into the so-called thematic dimension, and ii) the capability to make explicit the cause of similarity that generates each cluster. Ensemble techniques for combining dierent single-dimension cluster sets into a sort of multi-dimensional view of the considered linked data are also presented as a further contribution of the paper. Application to linked data summarization and exploration is nally discussed.