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Erschienen in:
Structure Inference for Linked Data Sources Using Clustering Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Dimensional Clustering of Linked Data: Techniques and Applications

verfasst von : Alfio Ferrara, Lorenzo Genta, Stefano Montanelli, Silvana Castano

Erschienen in: Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX

Verlag: Springer Berlin Heidelberg

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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.

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Fußnoten
1
For the sake of readability, only a subset of the available properties is reported (http://​www.​dbpedia.​org).
 
2
More technical details about the construction of linked data items from the RDF statements of a repository \(\mathcal {R}\) are provided in [5].
 
3
Since \({\text {ldi-match}}^{\mathcal {D}}(ldi_i, ldi_j) = {\text {ldi-match}}^{\mathcal {D}}(ldi_j, ldi_i)\), we define \(\sigma M\) and \(\pi M\) as upper triangular matrices.
 
4
A detailed presentation of summarization techniques is out of the scope of this work. Here, we outline how to generate a summary-view over a cluster set \(CL\). For the interested reader, a more technical presentation of cluster essential definition, proximity-link specification, and prominence value calculation is provided in [5].
 
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Metadaten
Titel
Dimensional Clustering of Linked Data: Techniques and Applications
verfasst von
Alfio Ferrara
Lorenzo Genta
Stefano Montanelli
Silvana Castano
Copyright-Jahr
2015
Verlag
Springer Berlin Heidelberg
Buch
Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX

Print ISBN: 978-3-662-46561-5

Electronic ISBN: 978-3-662-46562-2

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-662-46562-2_3

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