## 5 Experiments
### Model Validation
As illustrated by the heat map in Figure 11, Enhanced TransD achieves highly significant improvements (\(p<0.01\)) over TransE and TransR on nearly all metrics, most notably in Mean Rank (MR) and Hits@10. Comparisons with TransH and the original TransD reveal consistent mild to moderate gains (small \(p\)-values), further validating the synergistic effect of attribute projection and dual-limit loss in enhancing discriminatory power. Notably, under the strict Hits@1 criterion on WN18 RR--which poses challenges due to prevalent inverse relations--Enhanced TransD also significantly outperforms TransH and TransR (\(p<0.01\)), underscoring its resilience. Overall, the Nemenyi analysis quantifies and visually substantiates the superior efficacy of Enhanced TransD, particularly in addressing complex inverse and symmetric relations, while also highlighting the intrinsic difficulty of low-k retrieval on inverse relation-dense datasets. To comprehensively assess the independent contributions and complementary effects of each enhancement module, we designed four ablation variants of the baseline TransD model: (1) the original TransD; (2) TransD with only the attribute projection module; (3) TransD with only the dual-limit loss module; and (4) the fully enhanced TransD combining both modules. All experiments were conducted on the FB15K-237 and WN18 RR benchmarks. Observing that model performance stabilized around an embedding dimensionality of 200, we fixed this dimension for the ablation study. As shown in Figure 12, both the attribute projection and dual-limit loss modules yield significant gains over the original TransD, albeit with differing emphases: the attribute projection module--by explicitly encoding inherent entity attributes into the dynamic mapping matrices--enhances semantic  
Figure 11: Heat map of Friedman test results across evaluation metrics. discrimination in the embedding space, leading to superior performance on ranking-quality metrics such as MRR and Hits@1; the dual-limit loss module--by imposing independent upper and lower bounds on positive- and negative-sample scores--optimizes mean rank and Hits@K, improving retrieval precision and recall. When combined, the fully enhanced TransD model achieves the best results across all evaluation metrics, thereby validating the complementarity and synergistic value of the two strategies. Notably, the attribute projection module provides fine-grained representations of domain attributes, reducing confusion among semantically similar triples; the dual-limit loss module enforces clearer separation at decision boundaries, mitigating misclassification in challenging cases and enhancing generalization. Their joint application yields a balanced improvement in both precision and recall, with stable, sustained performance gains and no signs of overfitting. Moreover, consistent results across datasets of varying scale and complexity further attest to the stability and generalizability of the proposed approach. In summary, the enhanced TransD model achieves outstanding performance on the benchmark datasets FB15K-237 and WN18 RR, delivering stable and superior results in entity ranking, accuracy, F1 score, and various Hits@k metrics. These findings not only validate the model's effectiveness in capturing complex semantic relationships among entities but also demonstrate its robustness against data sparsity and noise. Building on this foundation, we will next apply the enhanced TransD model to representation learning on the constructed California Traffic Accident Knowledge Graph, with the aim of uncovering latent semantic patterns in real-world accident data and thereby providing a solid theoretical and practical basis for optimizing accident prevention and safety management strategies.