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Describing Semantics of Data Metamodels: A Case Study of Association-Oriented Metamodel

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Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12876))

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Abstract

In this paper, we describe the method for expressing the semantics of data metamodels using a concept system. The method abstract from metamodels’ syntax and deems to enable the modeler to compare different data metamodels, and express semantics-aware translations between different metamodels. The method is based on the Semantics Of Business Vocabulary And Rules standard. Moreover, the paper describes the Association-Oriented Metamodel as a case study for the method.

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Notes

  1. 1.

    A monadic semantic atom is a semantic atom that only references one concept corresponding to a metamodel category. For example: . Monadic semantic atoms are used to represent properties or characteristics of concepts.

  2. 2.

    A polyadic semantic atom is a semantic atom that references two or more concepts. For example: . Note that in this method the term polyadic also applies to dyadic atoms.

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Authors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wybrzeże Stanisława Wyspiańskiego 27, 50-370, Wrocław, Poland

    Marcin Jodłowiec & Marek Krótkiewicz

Authors
  1. Marcin Jodłowiec
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  2. Marek Krótkiewicz
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Corresponding author

Correspondence to Marcin Jodłowiec .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Democritus University of Thrace, Kimmeria, Xanthi, Greece

    Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Jodłowiec, M., Krótkiewicz, M. (2021). Describing Semantics of Data Metamodels: A Case Study of Association-Oriented Metamodel. In: Nguyen, N.T., Iliadis, L., Maglogiannis, I., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2021. Lecture Notes in Computer Science(), vol 12876. Springer, Cham. https://doi.org/10.1007/978-3-030-88081-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-88081-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88080-4

  • Online ISBN: 978-3-030-88081-1

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