## 4 Results and Discussion
### Comparison and Analysis  
In order to validate the effectiveness of the SHO-BP algorithm, this study applies other computational methods to predict semantic similarity for the same set of test samples. Pearson correlation coefficient analysis and error analysis are conducted to compare the  
Figure 5: Flowchart of model training and testing (the concepts involved in training and testing in the figure are examples). Figure 6: Regression curves for training and testing sets. Figure 7: The SHO-BP algorithm result and evaluation comparison curve (**above**), error curve (**below**). performance of these methods. Considering that there are currently no specific ontology-based semantic similarity calculation methods tailored for the domain of transportation infrastructure, we selected and compared our proposed method with five commonly used ontology-based semantic similarity calculation methods. The five computational methods are as follows: Whale Optimization Algorithm optimized BP neural network method (WOA-BP) [27], Improved Edge-Counting method [14], Improved IC (Information-Content) method [28], Hierarchical Ontology Model (HOM) [29] method, and HowNet [30] method. These five methods possess distinct characteristics, all of which can be employed to measure the semantic association among concepts within a domain. The partial comparison results obtained from different methods are presented in Table 1, where the similarity evaluation values serve as the prediction targets. As shown in Table 1, the results obtained by the SHO-BP algorithm are closer to the evaluation values compared to other methods. Both SHO-BP and WOA-BP utilize optimization algorithms to improve the performance of the BP neural network. While the results of the SHO-BP and the WOA-BP are relatively close, the SHO-BP algorithm demonstrates better performance by achieving results that are even closer to the evaluation values. We also compared the Pearson correlation coefficients and errors obtained from different calculation methods, as shown in Tables 2 and 3. The Pearson coefficient is used to measure the linear correlation between results and evaluation values, where a higher Pearson coefficient indicates better algorithm performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Concept Pair** & **Evaluation** & **SHO-BP** & **WOA-BP** & **Improved** & **IC** & **HOM** & **HowNet** \\  & **Value** & & & & **Edge-Counting** & **Improved IC** & **HOM** & **HowNet** \\ \hline Bridge deck & 0.48475 & **0.41420** & 0.38288 & 0.34014 & 0.40324 & 0.39224 & 0.65855 \\ Bridge bearing & & & & & & & \\ \hline Concrete box girder & 0.73087 & **0.74099** & 0.75072 & 0.70862 & 0.69431 & 0.74978 & 0.57454 \\ Concrete T-beam & & & & & & & \\ \hline Abutment & 0.19581 & **0.21033** & 0.21257 & 0.12870 & 0.22043 & 0.16745 & 0.15145 \\ Arch segment & & & & & & & \\ \hline Transverse beam & 0.21445 & **0.20296** & 0.19764 & 0.17351 & 0.20922 & 0.17601 & 0.35266 \\ Suspender cable & & & & & & & \\ \hline Arch segment & 0.93627 & **0.92994** & 0.88600 & 0.95768 & 0.78764 & 0.97456 & 0.46754 \\ Steel frame arch & & & & & & & \\ \hline Tower column & 0.41634 & **0.41988** & 0.42420 & 0.34014 & 0.40220 & 0.39520 & 0.37844 \\ Steel anchor box & & & & & & & \\ \hline Bridge & 0.50807 & **0.50355** & 0.49456 & 0.47006 & 0.54469 & 0.46456 & 0.63891 \\ Bridge deck & & & & & & & \\ \hline Girder & 0.96123 & 0.92336 & 0.88468 & **0.99707** & 0.86346 & 0.80079 & 0.56754 \\ Concrete box girder & & & & & & \\ \hline Steel box girder & 0.84485 & **0.74868** & 0.74284 & 0.70862 & 0.70563 & 0.73213 & 0.60877 \\ Composite steel box girder & & & & & & \\ \hline Foundation Component & & & & & & & \\  Drilled pile & 0.32018 & **0.29312** & 0.27865 & 0.23401 & 0.28711 & 0.25655 & 0.35875 \\ \hline \end{tabular}
\end{table} 
Table 1: Semantic similarity calculated in different methods (the semantic similarity closest to the evaluation value within each row is highlighted in bold).