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Thematic Clustering and Exploration of Linked Data

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Search Computing

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7538))

Abstract

Now that a huge amount of data is available in the Linked Data Cloud, the need of effective exploration and visualization techniques is becoming more and more important. In this paper, we propose aggregation and abstraction techniques for thematic clustering and exploration of linked data. These techniques transform a basic, flat view of a potentially large set of messy linked data for a given search target, into a high-level, thematic view called inCloud. In an inCloud, thematic exploration is guided by few essentials auto-describing their prominence for the search target and by their reciprocal proximity relations.

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Authors and Affiliations

  1. DI - Università degli Studi di Milano, Via Comelico, 39, 20135, Milano, Italy

    Silvana Castano, Alfio Ferrara & Stefano Montanelli

Authors
  1. Silvana Castano
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  2. Alfio Ferrara
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  3. Stefano Montanelli
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica e Informazione,, Politecnico di Milano, Via Ponzio, 34/5, 20133, Milan, Italy

    Stefano Ceri

  2. Dipartimento di Elettronica e Informazione, Politecnico di Milano, 20133, Milan, Italy

    Marco Brambilla

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Castano, S., Ferrara, A., Montanelli, S. (2012). Thematic Clustering and Exploration of Linked Data. In: Ceri, S., Brambilla, M. (eds) Search Computing. Lecture Notes in Computer Science, vol 7538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34213-4_11

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  • DOI: https://doi.org/10.1007/978-3-642-34213-4_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34212-7

  • Online ISBN: 978-3-642-34213-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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