Bridging Textual and Graphical Representations with Knowledge Graphs in Modeling Languages

This paper introduces a novel approach for transforming models between textual and graphical representation formats using knowledge graphs. We apply this method within an Identity and Access Management (IAM) environment, utilizing a context-specific modeling language to define interactions among IAM components and actors in a socio-technical system. Our approach emphasizes communicative interactions and environmental constraints of the different communicators, supporting distinct grammars for both representation forms. Additionally, we delve into knowledge graph methodologies for model-based transformations, developing transformation patterns to facilitate this process. By leveraging knowledge graph patterns, we ensure adaptable and precise model transformations, significantly improving interoperability in model-driven engineering. This flexibility makes knowledge graphs well-suited for accommodating various language constructs.

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Publication Details

Published
2025-04-04
DOI
https://doi.org/10.1109/icosse65712.2025.00024
Primary Topic
Natural Language Processing Techniques
Type
article
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article

Bridging Textual and Graphical Representations with Knowledge Graphs in Modeling Languages

Johannes Schick, Marc Wagner, Wolfram-Manfred Lippe
Natural Language Processing Techniques
article

Bridging Textual and Graphical Representations with Knowledge Graphs in Modeling Languages

Johannes Schick, Marc Wagner, Wolfram-Manfred Lippe
article en

Abstract

This paper introduces a novel approach for transforming models between textual and graphical representation formats using knowledge graphs. We apply this method within an Identity and Access Management (IAM) environment, utilizing a context-specific modeling language to define interactions among IAM components and actors in a socio-technical system. Our approach emphasizes communicative interactions and environmental constraints of the different communicators, supporting distinct grammars for both representation forms. Additionally, we delve into knowledge graph methodologies for model-based transformations, developing transformation patterns to facilitate this process. By leveraging knowledge graph patterns, we ensure adaptable and precise model transformations, significantly improving interoperability in model-driven engineering. This flexibility makes knowledge graphs well-suited for accommodating various language constructs.

University of Ulster (GB), University of Augsburg (DE)
Quality Education
Openalex Percentile: Top 89%
Natural Language Processing Techniques
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Bridging Textual and Graphical Representations with Knowledge Graphs in Modeling Languages — Johannes Schick, Marc Wagner, et al. · (2025) | TGRS Research Map | TGRS