## 2 State-of-the-Art--Knowledge Gap and Motivation
ISA-95 [30] is one of the essential standards in the field of integration of enterprise control systems and serves as a widely used basis for designing Industry 4.0 [31], IIoT (Industrial Internet of Things) [1] or smart factories [32] related to MES and MOM (Manufacturing Operations Management). To create a semantically integrated design concept, the production capability and personnel models of the ISA-95 standard are recommended as a basis for modeling. B2 MML is an implementation of IEC/ISO 62264 [33] to provide a freely available XML for manufacturing companies [34]. B2 MML standard elements are recommended for the development of problem-specific ontologies, such as the concept of collaborative assembly workstations [35], where semantic technologies are used to improve interoperability with external legacy systems such as ERP and MES. AutomationML [36] aims to standardize the exchange of data in the engineering process of production systems. In an AutomationML environment, the IEC 62264-2 personnel model [37] provides a method to model the operator in a production process with the following elements: personnel class, personnel class property, person and person property. AutomationML is also recommended as an exchange file format as a step toward automated job design based on optimized resource allocation [38]. Another important point to consider is that human-centric Cyber-Physical Production Systems (CPPSs) in smart factories and active collaboration between humans and machines have introduced an ontological framework known as the PSP ontology (Problem, Solution, Problem-Solver Ontology) [39]. The research focused on integrating the three main concepts of \"Problem-Solving Semantically Profile\", \"Problem-Solver Profile\", and \"Solution Profile\". In addition to the semantic representation and reasoning of these core concepts, the study introduced the contingency vector, competence and autonomy vectors, and the solution maturity index for CPPS [39]. Moreover, due to the insufficient operator-based models, particularly in decision-making aspects [40], it is recommended to incorporate the human operator model into the shop floor control system. Enhancing human-machine interaction through ontologies is recommended. To prioritize human well-being while ensuring production efficiency, the development of a human-centred intelligent environment requires the consideration of various factors. There is a high demand for identifying suitable factors to evaluate human-robot collaboration and ergonomic conditions for factory workers [41]. A comprehensive framework is required to evaluate Human-Machine Interfaces (HMI) and Human-Robot Interactions (HRI) in collaborative manufacturing settings [42]. A comprehensive systematic review [15] categorized the measures, indicators, and quality factors used in the HRI literature using a methodical approach. The indicators are grouped into categories relevant to industry 5.0 research, including physical ergonomics (safety, physical workload, job design), cognitive ergonomics (mental workload, awareness), performance (efficiency, effectiveness), and user experience satisfaction/hedonomics (emotional responses, acceptance, attitudes, trust) [15]. In the field of ontology engineering, especially within the Industry 4.0 framework, a structured series of methodical procedures is adhered to [43]. Initially, a comprehensive analysis of requirements is performed to grasp the diverse points of view of stakeholders and establish the goals, extent, and requirements of the ontologies [44]. Before embarking on any new development, existing ontological resources are evaluated for potential repurposing, focusing on efficiency and the integration of well-established concepts. Throughout the development phase, specialized tools are utilized to define essential elements such as classes, relationships, and axioms. A thorough assessment process, involving expert evaluations and automated validations, ensures alignment with recognized standards and specifications.