## 7 Conclusions  
In this study, an ontological framework integrating CBR and NLP was proposed to facilitate knowledge retrieval and reasoning, which can be used to support decision-making in metro accident responses. First, an ontological model was developed to formalize the representation of domain knowledge, such as the regulations, emergency plans, and past accident records. Based on the ontology model, NLP techniques were adopted to automatically annotate accident cases into a set of keywords, which could enhance the efficiency of further case retrievals. Then, RBR was used as a complement to CBR because CBR may fail when there are few or no relevant cases. The combination of CBR and RBR can avoid this problem in the metro accident response by retrieving the specific knowledge found in a similar case or general knowledge found in regulations. SWRL rules were used in this study to represent the response measures in the regulations. Finally, the proposed framework was tested and evaluated through a case study via expert reviews. A total of 120 metro accidents in the operational phase were used to build a case library. Then, five experts in a metro operating company were invited to evaluate the performance of the proposed method in 10 testing queries based on the \"precision\" metric. Considering the demand for quick responses, users tend to adopt solutions in the case with the highest similarity to the input query. Thus, the accuracy of cases with the highest similarity was also verified in 10 testing queries. The results show the good performance of the proposed method.  
However, this study still has some limitations that may be alleviated in future studies. The limitations are summarized as follows.  
First, the ontology development is time-consuming and considerable initial work needs to be done. A significant manual effort is required to update the ontology model if the metro accident knowledge base is updated.  
Second, the proposed framework is limited in terms of case retrieval within a small case database (120 accident records). The main purpose of this study was to develop a general framework based on ontological CBR and NLP for supporting decision-making when facing emergent accidents rather than a complete accident case database. However, the small database would affect the retrieval results. To apply the proposed framework in practice, the size of the database should be extended in the future. Moreover, with the increasing number of stored accidents in the database, the pattern and trends of historical accidents can be identified through statistical methods, which can be used to make possible predictions of future accidents.  
Third, the proposed framework has not been validated in practice. To promote its application, a system with a convenient interface should be further developed.  
Conceptualization, B.Z. and H.W.; Methodology, H.W.; Software, L.J.; Validation, H.W. and L.J.; Writing--original draft preparation, H.W., B.Z., and X.X.; Writing--review and editing, H.W., B.Z. and B.M.; Funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research is partly supported by the National Natural Science Foundation of China (grant nos. 51878311, 71732001, and 51978302)  
The authors declare no conflict of interest.