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 different 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 finally discussed.
Dimensional clustering of Linked Data : techniques and applications / A. Ferrara, L. Genta, S. Montanelli, S. Castano. - In: TRANSACTIONS ON LARGE-SCALE DATA- AND KNOWLEDGE-CENTERED SYSTEMS. - ISSN 1869-1994. - 19:(2015 Feb 24), pp. 55-86. [10.1007/978-3-662-46562-2_3]
Dimensional clustering of Linked Data : techniques and applications
A. FerraraPrimo
;L. GentaSecondo
;S. MontanelliPenultimo
;S. CastanoUltimo
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 different 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 finally discussed.File | Dimensione | Formato | |
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