###### Abstract  
Metro accidents are apt to cause serious consequences, such as casualties or heavy economic loss. Once accidents occur, quick and accurate decision-making is essential to prevent emergent accidents from getting worse, which remains a challenge due to the lack of efficient knowledge representation and retrieval. In this research, an ontological method that integrates case-based reasoning (CBR) and natural language processing (NLP) techniques was proposed for metro accident case retrieval. An ontological model was developed to formalize the representation of metro accident knowledge, and then, the CBR aimed to retrieve similar past cases for supporting decision-making after the accident cases were annotated by the NLP technique. Rule-based reasoning (RBR), as a complementary of CBR, was used to decide the appropriate measures based on those that are recorded in regulations, such as emergency plans. A total of 120 metro accident cases were extracted from the safety monthly reports during metro operations and then built into the case library. The proposed method was tested in MyCBR and evaluated by expert reviews, which had an average precision of 91%. metro accident; ontology; CBR; NLP; accident response +
Footnote †: journal: Medical  
deemed the most effective resources for supporting emergency decision-making as part of a time-limited response strategy plan [6]. It is feasible for managers to obtain support through an intelligent knowledge system that can integrate the valuable knowledge of experts and real cases. Case-based reasoning (CBR) is prevailing as a decision-support approach in construction safety management, which applies the experience of similar past accidents to the new accident. For example, [PERSON], et al. [7] proposed a CBR-based method to identify safety risks by analyzing the precursors installed on the subway system. [PERSON] and [PERSON] [8] developed a CBR-based online system that can support the design and selection of an active fall protection system. [PERSON] et al. used the CBR method to retrieve similar historical cases to support decision-making in the response to risks connected with urban water supply networks [4]. However, in the metro operation stage, knowledge retrieval is still a huge challenge to the current CBR methods. Two critical issues need to be solved to improve the performance of knowledge retrieval: one is how to effectively represent the domain knowledge and the other is how the knowledge can be retrieved quickly and accurately from the database. Considering the former obstacle, most knowledge concerning metro accidents is in various unstructured forms (e.g., accident records, expert experience, and emergency plans). For instance, in China, the majority of the accident records and more than 80% of the emergency plans of metro operations are plain-text documents or rigid electronic files. Non-structured or semi-structured knowledge restricts the automation of decision-making progress regarding metro accidents. Ontology is the solution to this obstacle. Ontologies provide a way to formally represent the domain knowledge by defining the concepts, relations, and axioms [9]. Ontologies provide non-ambiguous semantic representation to domain knowledge. However, there are still some gaps in knowledge concerning the combination of ontology technology and CBR for decision-making regarding metro accident responses. Regarding the second issue, the annotation of past accidents is a critical step for quick CBR-based retrieval from the database. As part of the routine inspection of metro operations, new accident reports continually need to be added into databases where accident records are written in everyday language using different expressions. However, manually annotating accident records is time-consuming and labor-intensive work.