Utilizing knowledge graphs for explainable artificial intelligence in manufacturing

doi:10.1080/0951192X.2026.2619782

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Utilizing knowledge graphs for explainable artificial intelligence in manufacturing

2026-01-01

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Fadi El Kalach

Revathy Venkataramanan

Amit Sheth

Ramy Harik *

DOI：10.1080/0951192X.2026.2619782

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Abstract

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The innovations of Industry 4.0 have revolutionized the capabilities of modern manufacturing systems by integrating information technology (IT) and operational technology (OT). This convergence has unlocked unprecedented manufacturing potential, enabling advanced intelligence and autonomy. However, a critical challenge remains: the lack of transparency and explainability in the decision-making processes of these systems. Machine learning models, which are at the core of many Industry 4.0 advancements, often function as 'black boxes', providing decisions without clear reasoning behind them. This paper addresses this challenge by introducing a Knowledge Graph-based explainability framework to enhance the reasoning behind machine learning outputs. A process ontology was developed and applied to a robotic assembly line to provide clear reasoning behind the decisions of a classification algorithm. This framework provides technicians explanations behind the prediction of the classifier. This paper details the development of the framework and its successful deployment on a robotic assembly line. DrCIF was chosen as the classifier that identifies defective assembled rockets with an F1 score of 99% after training and testing. The output of this classifier is then explained through the deployed ontology, demonstrating its potential to bridge the gap between advanced manufacturing intelligence and decision-making transparency.

Keywords:

Smart manufacturing

explainable AI

knowledge graph

manufacturing ontology

time series analytics