Systematic Literature Review on Evolvable Knowledge Graphs in Manufacturing

This systematic literature review investigates how evolvable knowledge graphs (KGs) enhance manufacturing under Industry 4.0, identifying current solutions and learning approaches. 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 three main categories and four subcategories of learning approaches and related methods to change knowledge relations and nodes in evolvable KGs. The results contribute by updating the KG development process model and synthesizing understanding of evolvable KGs that utilize the three potential learning approaches: human-guided, machine-driven, and human-machine collaborative knowledge updates. Despite the 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 KG technologies in manufacturing. By deepening the understanding of how KGs can evolve, this review sets a conceptual basis for future research to develop more dynamic and intelligent systems tailored to the emerging demands of Industry 4.0 and 5.0.