## 5 Conclusions and Future Work  
This article introduces the design idea of a Human-Centred Knowledge Graph (HCKG) that is based on industry norms and semantic technologies related to Industry 5.0 advance  
Figure 16: Conceptual dashboard for human-centric manufacturing—competence and condition levels of operators and assets.  
ments. A block structure is presented as an enhanced version of the MOM model and the development framework. The study thoroughly considers the tasks performed by operators, encompassing movement assessment, collaboration with machines, work sequences, and ergonomic aspects. It is also emphasized that the integration of activity recognition technologies can enrich the valuable data within a knowledge graph in a smart factory setting. The issue of insufficient operator monitoring and assistance is discussed in the context of existing industry standards, advocating for a new human-centric approach to contemporary manufacturing practices. In the coming factories that use knowledge graphs, the data collection and knowledge exploration processes will be automated, thereby facilitating the creation of human digital twins and the adoption of Industry 5.0 technologies.  
Our objective was to summarize current methods and tools for semantic development and to introduce a concept for creating standard models of human-centered collaboration, illustrated through an industrial case study. The key contributions of this paper are as follows.  
* Emphasized the importance of incorporating human factors into cyber-physical systems.
* Proposed an expansion of automation standards (ISA-95, AutomationML, B2 MML) to include human-related processes and demonstrated the use of semantic technologies.
* The concept was validated through a replicable industrial case study. Various graph-based analyses were conducted using different types of graphs such as normal, directed, or hypergraphs, including resource allocation analysis, KPI evaluation, and the integration of a DAS.
* The application based on HCKG facilitated the identification of various forms of collaboration between human and machine actors in the assembly process.
* Furthermore, a conceptual design was put forward for a human-centric manufacturing dashboard.  
Future research will focus on enhancing the design of human-machine and human-human collaboration in manufacturing by implementing the HCKG concept in an intelligent environment. Several areas for improvement should be considered in future studies. One aspect is the incorporation of additional functionalities within the application block, such as an uncertainty simulator, a collaboration assessment tool, or an intelligent scheduling mechanism. Moreover, integrating HCKG into a digital twin and implementing closed-loop optimization and decision support could further strengthen the proposed approach. Lastly, it is crucial to encompass the entire HCKG pipeline and establish automated data retrieval within the shop floor and the semantic network.  
Conceptualization, J.A. and L.N.; methodology, L.N. and T.R.; validation, T.R. writing--original draft preparation, L.N. and T.R.; writing--review and editing, J.A.; visualization, L.N.; supervision, J.A. All authors have read and agreed to the published version of the manuscript.  
This work has been implemented by OTKA 143482 (Monitoring Complex Systems by goal-oriented clustering algorithms) with support provided by the National Ministry of Hungary for Culture and Innovation from the National Research, Development, and Innovation Fund. L.N. was supported by the UNKP-22-3 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development, and Innovation Fund.  
Not applicable.  
The related dataset is freely and fully available on the website of the authors: [[https://github.com/abonyllab/HCKG](https://github.com/abonyllab/HCKG)]([https://github.com/abonyllab/HCKG](https://github.com/abonyllab/HCKG)) (accessed on 14 March 2024).  
The authors declare no conflicts of interest.