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Swift Linked Data Miner Extension for WebProtégé

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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

Swift Linked Data Miner (SLDM) is a data mining algorithm capable to infer new knowledge and thus extend an ontology by mining a Linked Data dataset. We present an extension to WebProtégé providing SLDM capabilities in a web browser. The extension is open source and readily available to use.

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Notes

  1. 1.

    https://github.com/matthewhorridge/owlapi-gwt.

References

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Acknowledgement

This work was partially supported by the PARENT-BRIDGE program of Foundation for Polish Science, co-financed from European Union, Regional Development Fund (Grant No. POMOST/2013-7/8) and by Polish National Science Center, grant DEC-2013/11/N/ST6/03065.

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Authors and Affiliations

  1. Faculty of Computing, Poznan University of Technology, ul. Piotrowo 3, 60-965, Poznan, Poland

    Tomasz Sosnowski, Jedrzej Potoniec & Agnieszka Ławrynowicz

Authors
  1. Tomasz Sosnowski
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  2. Jedrzej Potoniec
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  3. Agnieszka Ławrynowicz
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Corresponding author

Correspondence to Jedrzej Potoniec .

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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Sosnowski, T., Potoniec, J., Ławrynowicz, A. (2017). Swift Linked Data Miner Extension for WebProtégé . 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_28

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  • DOI: https://doi.org/10.1007/978-3-319-58694-6_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58693-9

  • Online ISBN: 978-3-319-58694-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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