Domain Knowledge in Artificial Intelligence: Using Conceptual Modeling to Increase Machine Learning Accuracy and Explainability

TL;DR

This paper introduces CMML, a method leveraging domain knowledge via conceptual modeling to enhance data preparation, thereby improving machine learning accuracy and transparency in real-world applications.

Contribution

The paper presents a novel CMML approach that integrates conceptual modeling into data preparation for machine learning, addressing performance and explainability issues.

Findings

CMML improved model performance in two real-world case studies.

Data scientists found CMML applicable and beneficial for data preparation.

Enhanced transparency and understanding of machine learning models.

Abstract

Machine learning enables the extraction of useful information from large, diverse datasets. However, despite many successful applications, machine learning continues to suffer from performance and transparency issues. These challenges can be partially attributed to the limited use of domain knowledge by machine learning models. This research proposes using the domain knowledge represented in conceptual models to improve the preparation of the data used to train machine learning models. We develop and demonstrate a method, called the Conceptual Modeling for Machine Learning (CMML), which is comprised of guidelines for data preparation in machine learning and based on conceptual modeling constructs and principles. To assess the impact of CMML on machine learning outcomes, we first applied it to two real-world problems to evaluate its impact on model performance. We then solicited an assessment by data scientists on the applicability of the method. These results demonstrate the value of CMML for improving machine learning outcomes.