Ontologies and Knowledge Graphs for Railway Safety

Semantic technologies based on ontologies and knowledge graphs are increasingly recognized for their potential to enhance safety, risk, and emergency management in railway systems. This paper presents a systematic literature review aimed at identifying how ontologies, knowledge graphs, and the technologies based on them are applied within the domain of railway safety and assessing their contributions. A total of 53 relevant papers were analyzed using a structured review process, covering four main areas: risk management, safety management, emergency management, and accident analysis. The results reveal that ontologies and knowledge graphs support proactive hazard identification, formalization of safety knowledge, intelligent emergency response, and detailed accident causation modeling. Moreover, they enable semantic interoperability, reasoning, and automation across complex socio-technical railway systems. Despite their benefits, challenges remain regarding data heterogeneity, scalability, and the lack of semantic standardization. This study identifies the most relevant models and technologies, such as SRAC, SRI-Onto, and transformer-based graph neural networks, highlighting their role in advancing intelligent railway safety solutions. This work contributes a detailed map of the current state of semantic applications in railway safety and offers insight into emerging opportunities for future development.