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. Ferrara
Primo
;
L. Genta
Secondo
;
S. Montanelli
Penultimo
;
S. Castano
Ultimo
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.
Settore INF/01 - Informatica
24-feb-2015
Article (author)
File in questo prodotto:
File Dimensione Formato  
bok%3A978-3-662-46562-2.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 10.87 MB
Formato Adobe PDF
10.87 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
chp%3A10.1007%2F978-3-662-46562-2_3.pdf

accesso riservato

Tipologia: Publisher's version/PDF
Dimensione 1.95 MB
Formato Adobe PDF
1.95 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/272388
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? ND
social impact