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Enhancing Research Clarity: Ontology-Based Modeling of Argumentation in RPML

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Advanced Information Systems Engineering Workshops (CAiSE 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 521))

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Abstract

Navigating the research process, from problem identification to argumentation construction, challenges novice researchers. This study introduces RPML (Research Problem Modeling Language), a metamodel and ontology designed to address these challenges by visually representing key aspects of research argumentation. RPML enhances clarity and coherence in research discourse by providing researchers with a visual representation of argumentation for a research problem. RPML is represented as a specialization of the OMG Business Motivation Model and Toulmin’s argumentation model approach. This enables researchers to gain a comprehensive overview of their research projects, identify research problems, build robust argumentation, and select suitable research strategies.

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Author information

Authors and Affiliations

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

    Knut Hinkelmann, Valeriia Afonina & 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. Knut Hinkelmann
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  2. Valeriia Afonina
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  3. Devid Montecchiari
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Correspondence to Knut Hinkelmann .

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Editors and Affiliations

  1. Federal University of Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  2. Utrecht University, Utrecht, The Netherlands

    Claudio Di Ciccio

  3. University of the Aegean, Mytilene, Greece

    Christos Kalloniatis

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Hinkelmann, K., Afonina, V., Montecchiari, D. (2024). Enhancing Research Clarity: Ontology-Based Modeling of Argumentation in RPML. In: Almeida, J.P.A., Di Ciccio, C., Kalloniatis, C. (eds) Advanced Information Systems Engineering Workshops. CAiSE 2024. Lecture Notes in Business Information Processing, vol 521. Springer, Cham. https://doi.org/10.1007/978-3-031-61003-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-61003-5_8

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