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In this work, we report on recent developments regarding the dynamic adaption of metamodels at runtime. This new approach is complemented by AdoPy, a Python-based wrapper for metamodel adaption procedures that also facilitates RDF-driven modifications and extensions. The conceptualization and implementation of the approach leverage knowledge graphs to extract relevant classes, relationships, and attributes, enabling the dynamic adaption of modeling method libraries. By integrating these capabilities into ADOxx, the proposed solution links metamodeling and knowledge graphs with systems engineering.

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Dynamic Adaption of Metamodels Based on Knowledge Graphs

  • 会议论文

  • 发表日期：

    2025年

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  • Danial M. Amlashi,
  • Alexander Voelz,
  • Junsup Song

摘要

In this work, we report on recent developments regarding the dynamic adaption of metamodels at runtime. This new approach is complemented by AdoPy, a Python-based wrapper for metamodel adaption procedures that also facilitates RDF-driven modifications and extensions. The conceptualization and implementation of the approach leverage knowledge graphs to extract relevant classes, relationships, and attributes, enabling the dynamic adaption of modeling method libraries. By integrating these capabilities into ADOxx, the proposed solution links metamodeling and knowledge graphs with systems engineering.