Gathering experience and organizing knowledge from a large number of engineering construction projects is conducive to more effective and efficient safety risk management in construction projects. Metro construction practitioners often find it difficult to determine what professional knowledge is needed to establish better management. By constructing the knowledge structure of safety risk management, which is composed of domain knowledge entities (DKEs) and their hierarchical relations,

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... oners can systematically master the knowledge of safety management, enhance safety management levels, and reduce the occurrence of accidents. Traditionally, domain knowledge structure was determined by experts, the mistakes occur due to the limitations of individual knowledge, and high time costs are unavoidable due to the massive amount of data. Therefore, in this study, we used a rule-based Chinese-language natural language processing (C-NLP) method to automatically extract the hierarchical relations between DKEs from a large dataset of unstructured text documents; we aimed to clarify the affiliation relationship and parallel relationship between DKEs. First, 68,817 sources of literature written in Chinese were collected. Next, the specific syntactic structures of relations of the DKEs were analyzed. Hierarchical extraction rules, including 16 hyponymic indicators and 8 appositive indicators, were revealed based on the linguistic characteristics. Then, the relations were extracted from test dataset. The precision and recall values were used to verify the model. Finally, the hierarchical relations of all the DKEs were extracted, and the knowledge structure was formed. The proposed method of hierarchical relation extraction contributes to the quick automatic construction of knowledge structures and minimizes expert bias. The knowledge structures can be used to guide safety training and can assist practitioners in safety risk management.