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Enriching Enterprise Architecture Models with Healthcare Domain Knowledge

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Advanced Information Systems Engineering Workshops (CAiSE 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 482))

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Abstract

Enterprise architecture (EA) modeling gives an opportunity to have an overview of the enterprise architecture supporting business-IT alignment within the rapidly changing environment. Visual representation of enterprise architecture models is appropriate for interpretation by humans. Machines, however, cannot interpret labels associated with the model element, as well as its domain-specific concepts. To make EA models machine-interpretable, a graphical representation of models shall be connected to domain knowledge. This research demonstrates an approach to enriching the EA model of a medical institution with healthcare domain knowledge. Evaluation of the developed solution proves that a human and a machine could equally understand the ontology-based EA model.

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Authors and Affiliations

  1. FHNW University of Applied Sciences and Arts Northwestern Switzerland, Windisch, Switzerland

    Valeriia Afonina, Knut Hinkelmann & Devid Montecchiari

  2. School of Science and Technology, UNICAM University of Camerino, Camerino, Italy

    Devid Montecchiari

  3. Department of Informatics, University of Pretoria, Pretoria, South Africa

    Knut Hinkelmann

Authors
  1. Valeriia Afonina
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  2. Knut Hinkelmann
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  3. Devid Montecchiari
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Corresponding authors

Correspondence to Valeriia Afonina , Knut Hinkelmann or Devid Montecchiari .

Editor information

Editors and Affiliations

  1. Zurich University of Applied Sciences, Winterthur, Switzerland

    Marcela Ruiz

  2. University of Haifa, Haifa, Israel

    Pnina Soffer

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Afonina, V., Hinkelmann, K., Montecchiari, D. (2023). Enriching Enterprise Architecture Models with Healthcare Domain Knowledge. In: Ruiz, M., Soffer, P. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2023. Lecture Notes in Business Information Processing, vol 482. Springer, Cham. https://doi.org/10.1007/978-3-031-34985-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-34985-0_2

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