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Machine Learning-assisted Domain Modeling: Survey, Classification, and Evaluation of Existing Approaches

Art der Arbeit:
Bachelorarbeit Wirtschaftsinformatik
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    Conceptual modeling, although crucial to any enterprise, can be a troublesome and error-prone activity. The creation and maintenance of conceptual models can be time-consuming and low-quality models can negatively affect organziational decision-making. One root problem is that domain experts often lack respective modeling expertise and, vice versa, modeling experts often lack the the required domain expertice. Therefore, it is desirable to support conceptual modeling activities by computational means. The rise of machine learning has led to broad range of suggestions how this might be achieved. The suggestions thereby vary in scope and domain-specificty and, among others, conern the repairment or completion of conceptual models. I summarize these different ML-based approaches to support conceptual modeling under the name machine learning-assisted domain modeling, or MAD modeling for short.

    This thesis should present a survey and classification of existing MAD modeling approaches. The core of thesis, or its main difficulty, is in the specification of an appropriate utility measure that supports an assessment of the diverse MAD modeling approaches.


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