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Research in conceptual modeling has led to such a vast and diverse body of publications that it is difficult for any one research group to digest it all. Approaches such as the Characterizing Conceptual Modeling Research framework have been proposed to help organize these contributions, but it is unrealistic to expect human experts to manually characterize all of this research. LLM-based generative AI tools like ChatGPT offer the promise of helping with this tedious manual work. In this paper, we show how to create and tune a customized version of ChatGPT to help with this task. Using this approach, it is feasible to create a truly usable knowledge survey for the evolving body of conceptual modeling research contributions.

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An LLM Assistant for Characterizing Conceptual Modeling Research Contributions

  • 会议论文

  • 发表日期：

    2025年

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  • Stephen W. Liddle,
  • Heinrich C. Mayr,
  • Oscar Pastor,
  • Veda C. Storey,
  • Bernhard Thalheim

摘要

Research in conceptual modeling has led to such a vast and diverse body of publications that it is difficult for any one research group to digest it all. Approaches such as the Characterizing Conceptual Modeling Research framework have been proposed to help organize these contributions, but it is unrealistic to expect human experts to manually characterize all of this research. LLM-based generative AI tools like ChatGPT offer the promise of helping with this tedious manual work. In this paper, we show how to create and tune a customized version of ChatGPT to help with this task. Using this approach, it is feasible to create a truly usable knowledge survey for the evolving body of conceptual modeling research contributions.