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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Notes
- 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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Ferrara, A., Genta, L., Montanelli, S., Castano, S. (2015). Dimensional Clustering of Linked Data: Techniques and Applications. In: Hameurlain, A., Küng, J., Wagner, R., Bianchini, D., De Antonellis, V., De Virgilio, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XIX. Lecture Notes in Computer Science(), vol 8990. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46562-2_3
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