## 2 Literature Review
### CBR-Based Decision-Making  
CBR generally refers to the process of solving new problems based on experience with similar past cases [29]. It is suggested by [PERSON] [30] that the experiences and cases can be reused to facilitate decision-making when a manager faces similar problems. CBR is memory-based and stimulates the human thinking process, which has been always applied in problem-solving fields [31]. As a decision support tool, CBR is well suited to construction safety management considering the construction industry's large amount of historical experiences. For example, [PERSON], et al. [32] proposed a CBR system to provide design support for an injection mold design. [PERSON], et al. [33] proposed a CBR system for a hydro-generator design and developed a case base library in this system to facilitate the design process. [PERSON], et al. [6] used the CBR method to analyze the response to risks connected with the urban water supply network. Additionally, rule-based reasoning (RBR) is usually added as the complementary method of CBR in the decision-making process, considering that CBR may fail when no relevant case is available [34]. [PERSON] and [PERSON] [35] discussed the corresponding advantages and disadvantages of CBR and RBR, and pointed out that their integration is useful. [PERSON] and [PERSON] [36] presented a hybrid approach combining CBR with RBR for diagnosing multiple faults. [PERSON] and [PERSON] [8] presented a web-based CBR-RBR system to support the design of an active fall protection system. Typically, CBR contains four main processes: retrieve, reuse, revise, and retain, where retrieve is the most important process in any CBR system [7]. Finding an effective method for case representation ensures that domain knowledge can be acquired accurately and easily, thus laying a good foundation for efficient retrieval [32]. However, data regarding metro accident cases are usually stored in plain-text documents or rigid electronic files that are in unstructured formats, such as PDF files, Word processing files, or HTML pages. The non-structural data do not follow a machine-readable format, thereby leading to a poor semantic understandability of traditional CBR [32]. One potential solution to this problem is the development of a semantic framework based on ontologies. Ontology technology used by a CBR system has several functions, such as representing cases and facilitating similarity calculations. [PERSON], et al. [37] combined CBR, RBR, and ontology to develop a solution retrieval system for expert searches and problem diagnosis. [PERSON], et al. [38] proposed an ontology-based CBR information retrieval system that can improve the accuracy of case retrieval and facilitate the search process. [PERSON], et al. [39] developed a knowledge model based on ontology and then used CBR and RBR to support automated decision-making for the disassembly of mechanical products. [PERSON], et al. [40] applied an ontology-based CBR approach to developing a system that can help to build and operate historical railroad accidents. Previous studies paved the way to recognize the advantage of the combination of CBR and ontology techniques in the construction industry. However, few efforts have been conducted to build an ontological CBR framework to facilitate knowledge representation and retrieval for decision-making during metro operations. Therefore, by combining ontology with CBR, a research framework for supporting decision-making in the metro accident response was constructed, and the feasibility of the framework was verified using a metro case study. The main contributions of this study are as follows: first, an ontology model was developed for comprehensively formalizing the domain knowledge and thus providing a good foundation for metro accident retrieval. Second, we used the NLP techniques to semi-automate the annotation of metro accident cases into keywords defined in the ontology model for the following similarity calculation.