## 1 Introduction  
The global economy and the developers of MES (Manufacturing Execution System) and ERP (Enterprise Resource Planning) systems face the challenge of enhancing productivity while retaining human labor in the manufacturing sector [1]. With the increasing diversity and complexity of product lifecycle applications, there is a growing need to digitize knowledge related to various aspects of the industry, such as process planning, production, and design. It is suggested that the key drivers for transforming data into knowledge and advancing process automation through interoperable data will involve KGs (knowledge graphs), semantic web technologies, and multi-agent systems [2]. Effective representation and communication of domain knowledge are also vital for smart manufacturing. The primary focus of Industry 4.0 lies in achieving extensive digitization, while Industry 5.0 aims to merge cutting-edge technologies with human involvement, characterized as a value-driven approach rather than a technology-centred approach [3]. Industry 4.0 integrates digital technologies such as IoT, artificial intelligence, big data, and automation into manufacturing processes to improve their efficiency, productivity, and customization. By prioritizing connectivity, data exchange, and smart factories, it establishes a more adaptable and responsive manufacturing setting. Industry 5.0, an emerging concept based on Industry 4.0, reintroduces human-centric methods into manufacturing operations. In contrast to Industry 4.0, which focuses mainly on automation and machine-to-machine communication, Industry 5.0 emphasizes collaboration between humans and machines. The objective is to take advantage of the distinct strengths of humans and machines, such as creativity, problem-solving, and emotional intelligence, to drive increased levels of innovation, flexibility, and sustainability in manufacturing. Industry 5.0 strives to achieve a harmonious balance between technology and humanity, using advanced technologies while giving priority to human well-being, creativity, and empowerment. Furthermore, there is growing interest in research areas such as industrial humanization [4], sustainability, and resilience [5]. Within the context of Industry 5.0, the importance of a Knowledge Graph (KG) is underscored by its ability to represent and analyze intricate data related to human operators [6]. The networked data structure of this system effectively records essential operator-related elements like ergonomics, working conditions, and machine cooperation in a systematic manner. By utilizing ontologies, a structured understanding of human actions in the manufacturing setting is achieved, guaranteeing compliance with human limitations and abilities. The graph's dynamic query and visualization features support real-time monitoring and flexibility. Consequently, the Knowledge Graph (KG) has become a key instrument for enhancing human-centered approaches in intricate manufacturing environments [7]. The human-centered aspect of the Industry 5.0 idea [3] strives for improved human-machine interaction, envisioning robots integrated with the human mind to collaborate rather than compete [8]. Throughout history, humans have influenced cyberphysical systems (CPSs) significantly, playing a vital role in their establishment and advancement. Consequently, human intelligence stands out as a crucial and predominant element in intelligent manufacturing, aligning with the concept of human-cyber-physical systems (H-CPSs) [9]. To achieve a suitable level of human-machine fusion, the concept of operator 4.0 [10; 11] must be assessed. This concept promotes adaptive automation within collaborative human-automation work systems, fostering a socially sustainable manufacturing workforce. A more recent idea, the concept Resilient Operator 5.0 [12], explores improving the resilience of human operators to various workplace factors, thus facilitating the implementation of efficient smart manufacturing systems.