Pairing Conceptual Modeling with Machine Learning
This is an incremental proposal for researchers and practitioners in data science and conceptual modeling to improve project foundations.
The paper tackles the problem of integrating conceptual modeling with machine learning to enhance data science projects, proposing a framework and illustrating it with a healthcare application, but provides no concrete results or numbers.
Both conceptual modeling and machine learning have long been recognized as important areas of research. With the increasing emphasis on digitizing and processing large amounts of data for business and other applications, it would be helpful to consider how these areas of research can complement each other. To understand how they can be paired, we provide an overview of machine learning foundations and development cycle. We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects. The framework is illustrated by applying it to a healthcare application. For the inverse pairing, machine learning can impact conceptual modeling through text and rule mining, as well as knowledge graphs. The pairing of conceptual modeling and machine learning in this this way should help lay the foundations for future research.