Pairing Conceptual Modeling with Machine Learning

TL;DR

This paper explores how conceptual modeling and machine learning can be integrated to enhance data science projects, providing frameworks and examples, especially in healthcare, to foster future research in their combined use.

Contribution

It introduces a framework for integrating conceptual modeling into machine learning workflows and demonstrates its application in healthcare, highlighting reciprocal influences between the two fields.

Findings

Framework for incorporating conceptual modeling into data science.

Application example in healthcare demonstrating the framework.

Discussion of machine learning's impact on conceptual modeling through text and rule mining.

Abstract

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.