## 6 Discussion  
In this study, we conducted a series of experiments on our Enhanced TransD model across two public benchmark datasets (FB15K-237 and WN18 RR) as well as on a self-constructed Traffic Accident Knowledge Graph (TAKG). By combining ablation studies with statistical significance tests, we systematically evaluated the individual and joint contributions of the \"attribute projection vector\" and \"dual-limit scoring loss\" modules. The following analysis is organized around four dimensions: overall performance improvement, module functionality and complementarity, sensitivity to embedding dimensionality, and model robustness. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**dim** & **MR** & **MRR** & **H@1** & **H@3** & **H@10** & **Acc** & **F1** \\ \hline
50 & 984.8603 & 0.528903 & 0.480301 & 0.564064 & 0.613507 & 0.480301 & 0.648924 \\
100 & 984.4376 & 0.527122 & 0.474052 & 0.572557 & 0.613148 & 0.474052 & 0.643196 \\
150 & 990.6853 & 0.530165 & 0.479965 & 0.57(1) Overall Performance Improvement. On the FB15K-237 dataset, Enhanced TransD reduced the mean rank (MR) by approximately 30 positions compared to the original TransD, increased the Mean Reciprocal Rank (MRR) by 0.03-0.05, and achieved average gains of 0.10, 0.12, and 0.07 on Hits@1, Hits@3, and Hits@10, respectively. These results demonstrate that the attribute projection mechanism and the dual-limit scoring loss work synergistically to sharpen the model's ability to distinguish positive from negative triples. Similar improvements were observed on WN18 RR: MR dropped by over 25%, MRR rose by about 0.07, and Hits@k metrics increased by more than 0.10 on average, indicating that our enhancements generalize well to large, path-complex knowledge graphs. (2) Module Functionality and Complementarity. Ablation experiments revealed distinct roles for the two modules. The variant with only the attribute projection vector excelled on ranking-quality metrics (MRR and Hits@1), suggesting that explicitly embedding fine-grained domain attributes (e.g., \"accident severity\", \"road conditions\") helps distribute entities more coherently in semantic space, thereby reducing confusion among neighboring entities. The variant with only the dual-limit scoring loss yielded greater gains in MR and Hits@K (K \(>\) 1), underscoring the value of independently regulating positive and negative score boundaries to boost recall. When both modules were combined, all evaluation metrics reached their peak values, confirming their complementary effects on ranking precision and overall retrieval efficiency. (3) Sensitivity to Embedding Dimensionality. Across embedding dimensions, performance followed a \"gain-then-deteriorate\" curve. In the low-dimensional range (50-150 dimensions), capacity constraints limited the model to capturing only basic dynamic mapping features. As the dimension increased to 200-250, the additional semantic information introduced by the attribute projection vector, coupled with the boundary-control benefits of the dual-limit loss, yielded peak performance across all ranking and classification metrics. Beyond 250 dimensions, however, a slight performance decline appeared, reflecting the onset of parameter redundancy and overfitting. This trend highlights the necessity of balancing embedding dimensionality against computational cost to avoid excessive capacity that undermines generalization. (4) Model Robustness. Using the Friedman test followed by Nemenyi post-hoc comparisons, we observed that all major metrics attained \(\chi^{2}\) statistics corresponding to \(p<0.05\), strongly rejecting the null hypothesis of no performance differences among models. This confirms the statistical significance and robustness of the Enhanced TransD's improvements. Moreover, the ablation gains were consistently reproducible across datasets of varying scale and attribute complexity, demonstrating our method's general applicability and reliability for traffic accident knowledge graph embedding.