## 5 Similarity Measurement of the Accident Cases
### Local Similarity
#### 5.1.2 Similarity of Symbolic Attributes  
Given the ontology knowledge structure of the fault characteristic, for the symbolic attribute, the method aims to measure the comprehensive semantic similarity adopted from [PERSON] [45]. The computation of a comprehensive similarity between symbolic attributes consists of two parts: the tree similarity and the similarity of the upper attributes. The tree similarity based on the semantic tree reveals the direct similarity of the attributes, while the similarity of the upper attributes reveals the indirect similarity of the concepts. Both of them are measured by considering the ancestor-child and parent-child relationships in similar maximal paths. After determining the similarities based on the semantic tree and based on the upper attributes, the comprehensive similarity can be obtained based on the weighted average of these similarities. (1) Similarity measure based on the semantic tree  
Sim\({}_{t}\) denotes the similarity based on the semantic tree, which is the direct similarity between two concepts [46]. As shown in Equation (2), the similarity measure is based on the level of the attributes and the distance between them:  
\[\text{Sim}_{t}(X_{i},Y_{i}) =\left\{ \begin{array}{l}\frac{\alpha\times[dI(X_{i})+dI(Y_{i})]} {[dis(X_{i},Y_{i})\ +\ a]\times[\infty\times max\times]\times max(]dI(X_{i})-dI(Y_{i})]}, \alpha>0,X_{i}\
eq Y_{i},\\ 1,X_{i}=Y_{i}\end{array} \right. \tag{2}\]  
In Equation (2), the \(dI(X_{i})\) and \(dI(Y_{i})\) reveal the semantic level of the attributes, the \(dis(X_{i},Y_{i})\) is the minimum distance between \(X_{i}\) and \(Y_{i}\) in the hierarchy, and the \(maxdl\) is the maximum number of hierarchy conceptual structures. Additionally, \(\alpha\) is an adjustable parameter that is the semantic distance of similarity to 1, where \(\alpha\) is equal to 0.5 for a large number of experiments. Based on these parameters, the \(\text{Sim}_{t}(X_{i},Y_{i})\) can be calculated through Equation (2). When the value of \(\text{Sim}_{t}(X_{i},Y_{i})\) is 1, an equivalent relationship exists between the corresponding attributes \(X_{i}\) and \(Y_{i}\). (2) Similarity measure based on the upper attributes  
Sim\({}_{s}\) denotes the similarity of \(X_{i}\) and \(Y_{i}\) based on the upper attributes. It reveals the indirect similarity of \(X_{i}\) and \(Y_{i}\). Equation (2) is used to calculate the similarity of \(X_{i}\) and \(Y_{i}\) based on the upper attributes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Attribute Name** & **Type** & **Value** \\ \hline \multirow{3}{*}{Accident characteristic} & Train number & Symbol & A01, D03, E28, etc. \\  & Vehicle fault characterization & Symbol & Bogie failure, out of order, emergency braking, etc. \\  & Signal failure characterization & Symbol & SMC equipment failure, indication loss in turnout, etc. \\ \hline \multirow{3}{*}{Accident response} & Staff response & Symbol & Train dispatcher, station watch-keeper, etc. \\  & Driving mode & Symbol & Automatic (ATO) driving pattern, Protective Manual (PM) \\  & Steering order & Symbol & Clean off the train, standby state, back to the parking lot, etc. \\  & Response measure & Symbol & Re-input, reactivation, equipment changes, etc. \\ \hline \multirow{3}{*}{Post-accident assessment} & Time delay & Integer & Numerical information \\  & Train delay & Integer & Numerical information \\  & Train failure & Integer & Numerical information \\  & Clean off the train & Integer & Numerical information \\  & Accident cause & Symbol & Textual information \\  & Corrective action & Symbol & Textual information \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Attributes of signal system failure accidents. 3. Comprehensive similarity measure \(\text{Sim}_{c}\) denotes the comprehensive similarity, which refers to the final value of similarity between target attributes \(X_{i}\) and \(Y_{i}\).