## 6 Conclusions and Future Work
Furthermore, the (pre-)training procedure can incorporate human expert domain knowledge in the form of a Bayesian Neural Network (BNN) [60; 61] and/or a Structural Causal Model (SCM) [62; 63], thereby being an excellent practical example of the human-in-the-loop principle [64] due to the fact that humans are able to bring in conceptual understanding and contextual knowledge [65]. Nevertheless, the explainability aspect has to be addressed and fulfill the expectations of forest operation experts to the same-if not a greater degree-as the PGMs mentioned in Section 4.3. A very important future work would be the integration of knowledge graphs. Basically, knowledge graphs (KGs) [66] can encapsulate vast quantities of structured and unstructured data related to forestry operations, including geographical, environmental, and operational parameters. Researchers can enrich the analytical process by integrating these graphs with existing AI models such as TabPFN. This integration would allow one to leverage relational information and contextual knowledge, enhancing the accuracy and relevance of predictive models. Combined with AI, knowledge graphs could identify previously unnoticed patterns and relationships in the data, leading to a more comprehensive understanding of the factors contributing to accidents. The work of [PERSON] et al. (2024) [67] recently used ontologies for analyzing accidents in the construction industry where they used a domain word discovery algorithm to build a construction safety ontology and combined this ontology with a Text Convolutional Neural Network (TextCNN). Ontologies and knowledge graphs, while both crucial in the organization and representation of knowledge, have distinct differences and applications. An ontology is a formal and structured representation of concepts and their relationships within a domain, primarily focused on establishing a common understanding that can be shared among people and computational entities. Knowledge graphs, conversely, are expansive networks of interconnected entities and their attributes, often encompassing a variety of data sources, including both structured and unstructured data. They are less rigid in structure compared to ontologies and excel in scalability, flexibility, and integration with AI and machine learning technologies. This makes knowledge graphs particularly effective for handling large, complex datasets and for applications that require sophisticated data analytics and real-time information processing, surpassing the capabilities of traditional ontologies in practical, dynamic environments [66]. Another possible future work is the synergy of counterfactual explanations and knowledge graphs. This combination could provide a multi-faceted view of accident scenarios, encompassing both the specific details of individual cases and the broader operational context. Employing knowledge graphs to inform and generate counterfactual scenarios could result in more realistic and applicable safety recommendations. This integrated approach would be particularly beneficial in tailoring safety protocols to the unique conditions of specific forestry operations, thereby enhancing the overall effectiveness of occupational safety strategies. Conceptualization, F.H., K.S. and A.H.; methodology, F.H., A.S. and A.J. ; software, A.S. and A.J. ; validation, K.S. ; formal analysis, A.S. and A.J. ; investigation, F.H., A.S., A.H. and A.J. ; resources, A.H.; data curation, A.S.; writing--original draft preparation, F.H., A.S., A.J. and A.H.; writing--review and editing, F.H., A.S., A.J. and A.H.; visualization, A.S.; supervision, K.S. ; funding acquisition, A.H. All authors have read and agreed to the published version of the manuscript. Parts of this work have been funded by the Austrian Science Fund (FWF), Project: P-32554 explainable Artificial Intelligence. Not applicable. Data Availability Statement: Data is unavailable due to privacy restrictions. The authors declare that they have no conflicts of interest. This work does not raise any ethical issues.