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 way should help lay the foundations for future research.

长期以来，概念建模和机器学习都被认为是重要的研究领域。随着对商业和其他应用程序的大量数据的数字化和处理的日益重视，考虑这些研究领域如何相互补充将很有帮助。为了了解它们如何配对，我们提供了机器学习基础和开发周期的概述。然后，我们研究如何将概念建模应用于机器学习，并提出一个将概念建模纳入数据科学项目的框架。该框架通过将其应用于医疗保健应用程序来说明。对于反向配对，机器学习可以通过文本和规则挖掘以及知识图来影响概念建模。