# Knowledge Graph Alignment Network with Node-Level Strong Fusion
## 5 Experiments
### Experimental Results
#### 5.4.3 S-DBP15K Results  
On a more difficult dataset, S-DBP15K for SFEA was evaluated. The experimental results are shown in Table 5. The performance of SFEA is significantly better than that of the baseline models, which proves that the SFEA model still shows good results on the heterogeneous KGs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**DBP15K\({}_{\textbf{ZH-en}}\)**} & \multicolumn{3}{c}{**DBP15K\({}_{\textbf{JA-EN}}\)**} & \multicolumn{3}{c}{**DBP15K\({}_{\textbf{FR-EN}}\)**} \\  & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** \\ \hline SFEA & 0.625 & 0.764 & 0.675 & 0.645 & 0.771 & 0.691 & 0.753 & 0.845 & 0.786 \\ (w/o SF) & 0.818 & 0.924 & 0.859 & 0.840 & 0.941 & 0.877 & 0.923 & 0.979 & 0.945 \\ SFEA & 0.816 & 0.911 & 0.852 & 0.846 & 0.937 & 0.881 & 0.925 & 0.977 & 0.945 \\ (w/o IS) & **SFEA** & **0.855** & **0.931** & **0.883** & **0.876** & **0.953** & **0.904** & **0.942** & **0.983** & **0.959** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study of the proposed model (entity alignment).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**DBP15 KZH-EN**} & \multicolumn{3}{c}{**DBP15 KJA-EN**} & \multicolumn{3}{c}{**DBP15 KFR-EN**} \\  & **Hits@1** & **Hits@10** & **Hits@1** & **Hits@10** & **Hits@10** & **Hits@1** & **Hits@10** \\ \hline MTransE-R & 0.328 & 0.576 & 0.310 & 0.561 & 0.189 & 0.443 \\ BootEA-R & 0.453 & 0.854 & 0.414 & 0.798 & 0.302 & 0.604 \\ GCN-Align-R & 0.702 & 0.828 & 0.639 & 0.818 & 0.420 & 0.538 \\ HGCN-R & 0.703 & 0.854 & 0.650 & 0.836 & 0.425 & 0.566 \\ RDGCN-R & 0.785 & 0.861 & 0.718 & 0.845 & 0.476 & 0.582 \\
**SFEA** & **0.813** & **0.891** & **0.767** & **0.881** & **0.514** & **0.628** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance on relation alignment.  
The analysis is as follows.  
1. Comparing the experimental results of S-DBP15K and DBP15K, the performance of all models is degraded. This shows that with the lack of triple information, the structure information and relation information are greatly reduced, resulting in inaccurate entity representations learned by the models.
2. The SFEA achieves the best results in three different language environments. In the comparison models, BootEA and GCN-Align models perform very poorly on sparse datasets. BootEA and GCN-Align models rely too much on relation triples and entity structure, so entity alignment does not work well in sparse hard cases. The bootstrap iteration method is used in BootEA, and its effect is slightly better than the GCN-Align. RDGCN used structure information, relation information, and name information. When faced with insufficient structure information and relation information, attribute information in SFEA plays a certain role.
3. Comparing the experimental results of the deformation of the three variants of SFEA, the without strong fusion (w/o SF) has the largest drop, indicating that in the case of unbalanced structure information of KGs, the strong fusion method in the SFEA model plays a key role. The influence of insufficient structure information is alleviated by tightly merging various information at the node level. Without sub-tasks alignment module (w/o UE) and without iterative strategy (w/o IS) also plays a role in the stability of the model. They further optimize the entity representations.
4. NMN performs well in the comparison model because it adopts a neighborhood sampling strategy to alleviate the impact of data sparsity. Through the comparison of experimental results, the experimental results of (w/o IS) are already better than NMN, indicating the efficiency of the strong fusion and subtask update modules in the model.