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Demystifying Ontological Classification in Language Engineering

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Modelling Foundations and Applications (ECMFA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9764))

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Abstract

The introduction of ontological classification to support domain-metamodeling has been pivotal in the emergence of multi-level modeling as a dynamic research area. However, existing expositions of ontological classification have only used a limited context to distinguish it from the historically more commonly used linguistic classification. In important areas such as domain-specific languages and classic language engineering the distinction can appear to become blurred and the role of ontological classification is obscured, if not fundamentally challenged. In this paper we therefore examine critical points of confusion regarding the distinction and provide an expanded explanation of the differences. We maintain that optimally utilizing ontological classification, even for tasks that traditionally have only been viewed as language engineering, is critical for mastering the challenges in complex systems modeling including the validation of multi-language models.

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Authors and Affiliations

  1. University of Mannheim, B6, C2.11, Mannheim, Germany

    Colin Atkinson

  2. Victoria University of Wellington, P.O. Box 600, Wellington, 6140, New Zealand

    Thomas Kühne

Authors
  1. Colin Atkinson
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  2. Thomas Kühne
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Correspondence to Thomas Kühne .

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Editors and Affiliations

  1. IT University of Copenhagen, Copenhagen, Denmark

    Andrzej Wąsowski

  2. Volvo Group Trucks Technology, Gothenburg, Sweden

    Henrik Lönn

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Atkinson, C., Kühne, T. (2016). Demystifying Ontological Classification in Language Engineering. In: Wąsowski, A., Lönn, H. (eds) Modelling Foundations and Applications. ECMFA 2016. Lecture Notes in Computer Science(), vol 9764. Springer, Cham. https://doi.org/10.1007/978-3-319-42061-5_6

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  • DOI: https://doi.org/10.1007/978-3-319-42061-5_6

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-42061-5

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