# A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks
## 4 Experiments
### Efficiency Study of Entity Alignment Method  
In this section, we conducted a comparative evaluation of the efficiency of various models, and early stopping was applied to all models. Please refer to Tables 6 and 7 for details. It is worth mentioning that in Table 6, in order to objectively evaluate the running efficiency of each model, we measured the average execution time of training, validation, and testing during five runs. Among all the methods, MTransE has the lightest implementation with only two layers of entity and relation embeddings and a simpler scoring \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model/** & **EN-DE-15K-V1** & **EN-DE-15K-V2** & **EN-FR-15K-V1** & **EN-FR-15K-V2** \\ \hline \hline
**MTransE** & 520.89 + 618.73 + 33.35 & 407.15 + 562.43 + 40.33 & 469.68 + 598.83 + 29.64 & 572.99 + 552.66 + 32.64 \\
**JAPE** & 589.79 + 682.37 + 36.61 & 538.12 + 617.01 + 41.19 & 566.28 + 657.26 + 31.01 & 510.89 + 624.47 + 36.02 \\ IPTransE & 20,783.12 + 20,411.01 + 33.91 & 17,016.05 + 25,541.36 + 40.87 & 12,777.05 + 13,019.38 + 32.51 & 20,759.59 + 21,517.86 + 41.32 \\ RDSCN & 1911.26 + 6017.83 + 136.29 & 4239.44 + 2433.15 + 140.60 & 3901.33 + 3782.80 + 132.75 & 4206.56 + 7754.57 + 136.24 \\ SEA & 5035.92 + 609.29 + 24.22 & 5035.92 + 615.20 + 31.18 & 3443.45 + 2947.20 + 22.44 & 5780.64 + 3725.31 + 25.69 \\
**IMUSE** & 8429.14 + 1071.84 + 194.77 & 9830.71 + 117.57 & 12 + 207.65 & 8375.05 + 105.654.46 + 161.94 & 9965.91 + 122,245.45 + 132.78 \\ GCN-Align & 8276.27 + 9297.59 + 73.61 & 6503.66 + 10,508.86 + 100.04 & 5395.27 + 6911.49 + 100.01 & 6365.06 + 10,320.45 + 104.57 \\
**PCE-HGTRA** & 7290.52 + 10,545.98 + 26.56 & 7828.82 + 9892.24 + 31.76 & 5167.42 + 6095.57 + 25.95 & 4524.95 + 7217.73 + 27.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The train+validation+test time (execution time in seconds) of the method in each dataset. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{4}{c}{**EN-DE-15K-V1**} & \multicolumn{4}{c}{**EN-DE-15K-V2**} \\ \hline
**Model** & **Hits@1** & **Hits@5** & **Hits@10** & **MR** & **Hits@1** & **Hits@5** & **Hits@10** & **MR** \\ \hline
**PCE-HGTRA** & 62.63 & 76.77 & 80.84 & 68.74 & 64.97 & 80.76 & 85.52 & 15.93 \\
**PCE-HGTRA w/o** **FCE** & 51.85 & 69.71 & 76.71 & 175 & 62.68 & 78.87 & 84.72 & 20.15 \\
**PCE-HGTRA w/o** **HGTRA** & 43.38 & 55.31 & 59.47 & 828.38 & 58.57 & 69.83 & 73.35 & 560.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study of PCE-HGTRA on the EN-DE-15K-V1 and EN-DE-15K-V2 datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{4}{c}{**EN-FR-15K-V1**} & \multicolumn{4}{c}{**EN-FR-15K-V2**} \\ \hline
**Model** & **Hits@1** & **Hits@5** & **Hits@10** & **MR** & **Hits@1** & **Hits@5** & **Hits@10** & **MR** \\ \hline
**PCE-HGTRA** & 62.14 & 74.8 & 79.05 & 51.95 & 34.64 & 61.97 & 72.85 & 21.99 \\
**PCE-HGTRA w/o** **PCE** & 31.03 & 52.36 & 61.57 & 384.8 & 33.75 & 64.62 & 76.33 & 27.53 \\
**PCE-HGTRA w/o** HGTRA** & 20.12 & 33.28 & 38.27 & 1513.95 & 22.72 & 37.7 & 43.56 & 1209.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study of PCE-HGTRA on the EN-FR-15K-V1 and EN-FR-15K-V2 datasets. function, thus it has the shortest running time. Except for MTransE and JAPE, RDGCN achieved the best performance on the four datasets mentioned above due to its utilization of a wider entity neighborhood in the knowledge graph during training. This method requires more training time, i.e., longer epochs, and for specific information please refer to Table 7. However, overall, RDGCN's efficiency is considerable, and its convergence speed is faster. In addition, RDGCN is faster than our unsupervised method PCE-HGTRA, because PCE-HGTRA needs to iterate multiple times in the character embedding module, using all possible descriptive text values to reinforce the quality of embedding, while the latter can better adapt to situations where seeds are often unavailable in real-world environments.