Systematic Literature Review on Evolvable Knowledge Graphs in Manufacturing

<p>This systematic literature review investigates how evolvable knowledge graphs (KGs) enhance manufacturing. Evolvable KGs adapt to changing knowledge by leveraging machine learning algorithms and human expertise to improve decision-making, operational efficiency, and predictive maintenance capabilities beyond the capabilities of prevailing solutions enabled by dynamic KGs. This review maps the existing literature based on the stages of the KG development process. The main results are an updated model of the creation and maintenance of evolvable KGs in manufacturing and a literature-based synthesis of learning approaches for enabling KG evolution through knowledge processing and knowledge utilization. The results contribute to updating the KG development process model and synthesizing an understanding of evolvable KGs that utilize the observed learning approaches on knowledge processing to change KG relations and nodes, and three learning approaches through knowledge utilization (human-guided, machine-driven, and human-machine collaborative knowledge updates). Despite emerging advances, challenges persist in quality assurance, process planning, and integration of human expertise. The findings advocate addressing these issues to promote greater adoption and optimization of KGs in manufacturing. By deepening the understanding of how KGs can evolve through learning, this review sets a conceptual basis for future research to develop more dynamic and intelligent KG-based manufacturing systems.</p>