A Tale of Three Words: Knowledge, Safety, and Graphs

The growing complexity of modern systems has pushed safety science beyond traditional analysis methods. In a world where the unknown matters as much as the known, knowledge graphs emerge as a powerful means for representing, connecting, and extending knowledge. However, the intersection between safety science and knowledge graphs remains largely unexplored. Which communities of researchers are leveraging knowledge graphs for safety? Is there any common pattern in how they are being used? This paper addresses these questions by presenting a systematic review of the literature on the use of knowledge graphs in the context of safety. Based on 173 eligible documents, we propose a classification framework structured around three dimensions: the originality of knowledge characterization, the originality of knowledge extraction, and the maturity of safety analysis. The framework identifies three archetypes of knowledge graph users: Assemblers, who rely on existing models and tools; Alchemists, who adapt available knowledge structures or extraction procedures; and Shapers, who develop novel ontologies, extraction methods, or both. The obtained results show how the latter represents the largest group among the reviewed studies, suggesting a tension between analytical maturity and the need for customized solutions. More broadly, the classification framework presented in this review may support researchers from both the safety and the artificial intelligence communities in fostering a shared path for the scientific development of these disciplines.