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Modeling, Generating, and Publishing Knowledge as Linked Data

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10180))

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Abstract

The process of extracting, structuring, and organizing knowledge from one or multiple data sources and preparing it for the Semantic Web requires a dedicated class of systems. They enable processing large and originally heterogeneous data sources and capturing new knowledge. Offering existing data as Linked Data increases its shareability, extensibility, and reusability. However, using Linking Data as a means to represent knowledge can be easier said than done. In this tutorial, we elaborate on the importance of semantically annotating data and how existing technologies facilitate their mapping to Linked Data. We introduce [R2]RML languages to generate Linked Data derived from different heterogeneous data formats –e.g.,  DBs, XML, or JSON– and from different interfaces –e.g.,  files or Web apis. Those who are not Semantic Web experts can annotate their data with the RMLEditor, whose user interface hides all underlying Semantic Web technologies to data owners. Last, we show how to easily publish Linked Data on the Web as Triple Pattern Fragments. As a result, participants, independently of their knowledge background, can model, annotate and publish data on their own.

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Notes

  1. 1.

    https://www.w3.org/TR/swbp-xsch-datatypes/.

  2. 2.

    http://lov.okfn.org/dataset/lov/.

  3. 3.

    https://www.w3.org/TR/vocab-dcat/.

  4. 4.

    http://www.hydra-cg.com/spec/latest/core/.

  5. 5.

    https://www.w3.org/TR/sparql11-service-description/.

  6. 6.

    https://github.com/RMLio/RML-Mapper.

  7. 7.

    https://github.com/RMLio/RML-Validator.

  8. 8.

    https://okfn.org/opendata/.

  9. 9.

    https://creativecommons.org/publicdomain/zero/1.0/.

  10. 10.

    https://creativecommons.org/ns.

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Acknowledgements

The described research activities were funded by Ghent University, imec, the Flanders Innovation & Entrepreneurship (AIO), the Research Foundation – Flanders (FWO), and the European Union.

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Authors and Affiliations

  1. Ghent University – imec – IDLab, Ghent, Belgium

    Anastasia Dimou, Pieter Heyvaert, Ruben Taelman & Ruben Verborgh

Authors
  1. Anastasia Dimou
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  2. Pieter Heyvaert
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  3. Ruben Taelman
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  4. Ruben Verborgh
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Correspondence to Anastasia Dimou .

Editor information

Editors and Affiliations

  1. University of Bologna , Bologna, Italy

    Paolo Ciancarini

  2. University of Bologna , Bologna, Italy

    Francesco Poggi

  3. Stanford University , Palo Alto, California, USA

    Matthew Horridge

  4. Lancaster University , Lancaster, United Kingdom

    Jun Zhao

  5. Garvan Institute of Medical Research , Darlinghurst, New South Wales, Australia

    Tudor Groza

  6. Universidad Politecnica de Madrid , Boadilla del Monte, Spain

    Mari Carmen Suarez-Figueroa

  7. The Open University , Milton Keynes, United Kingdom

    Mathieu d'Aquin

  8. STLab ISTC-CNR , Rome, Italy

    Valentina Presutti

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Dimou, A., Heyvaert, P., Taelman, R., Verborgh, R. (2017). Modeling, Generating, and Publishing Knowledge as Linked Data. In: Ciancarini, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10180. Springer, Cham. https://doi.org/10.1007/978-3-319-58694-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-58694-6_1

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  • Online ISBN: 978-3-319-58694-6

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