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OTM Confederated International Conferences "On the Move to Meaningful Internet Systems"

OTM 2016: On the Move to Meaningful Internet Systems: OTM 2016 Conferences pp 709–726Cite as

Class Annotation Using Linked Open Data

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10033))

Abstract

The meaningful usage of RDF datasets requires a description of their content. Part of this description is provided in the dataset itself through class definitions. However, the name of a class does not always reflect accurately its semantics. This meaning can be captured by providing some annotations for each class.

In this paper, we present a set of algorithms exploiting the instances of a dataset in order to provide annotations which best capture the semantics of a class. These algorithms rely on an external knowledge source. We introduce three ways of extracting annotations: (i) using the names of instances, (ii) using their property sets and (iii) considering the vocabularies used by the dataset. As an external source, we have used Linked Open Data, which represents an unprecedented amount of knowledge provided on the Web. We also show how annotations can be used to discover a class hierarchy and we present some evaluation results showing the effectiveness of our approach.

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Notes

  1. 1.

    Conference: data.semanticweb.org/dumps/conferences/dc-2010-complete.rdf.

  2. 2.

    BNF: datahub.io/fr/dataset/data-bnf-fr.

  3. 3.

    DBpedia: dbpedia.org.

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Acknowledgments

This work was partially funded by the French National Research Agency through the CAIR ANR-14-CE23-0006 project.

Author information

Authors and Affiliations

  1. DAVID Laboratory, University of Versailles Saint-Quentin-en-Yvelines, Versailles, France

    Kenza Kellou-Menouer & Zoubida Kedad

Authors
  1. Kenza Kellou-Menouer
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  2. Zoubida Kedad
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Corresponding author

Correspondence to Kenza Kellou-Menouer .

Editor information

Editors and Affiliations

  1. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Christophe Debruyne

  2. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  3. TU Graz, Graz, Austria

    Robert Meersman

  4. La Trobe University, Melbourne, Australia

    Tharam Dillon

  5. Institute of Computer Languages, TU Wien, Vienna, Austria

    eva Kühn

  6. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Declan O'Sullivan

  7. Università degli Studi di Milano Crema, Crema, Italy

    Claudio Agostino Ardagna

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Kellou-Menouer, K., Kedad, Z. (2016). Class Annotation Using Linked Open Data. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_44

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  • DOI: https://doi.org/10.1007/978-3-319-48472-3_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48471-6

  • Online ISBN: 978-3-319-48472-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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