# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
## 3 Results
### Validating the Superiority of Our Method in Entity Alignment  
In this section, we compare the proposed method to six other entity alignment methods: MTransE [13], IPTransE [14], JAPE [15], BootEA [16], MultiKE [17], and KDCoe [18]. Tables 6 and 7 show the entity alignment performances of these methods on the DBP15K and DWY100K datasets, in terms of Hits@10, MR, and MRR. According to the experimental results, the proposed method outperformed all other methods in all metrics on the DBP15K and DWY100K datasets. As expected, MTransE performed poorly in the entity alignment task, considering it loses information when learning the mapping between different embedding spaces. Consequently, it could not compute the similarities between entities from different KGs. IPTransE and JAPE outperformed MTransEE, considering they can utilize relation paths and entity attributes. However, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**ZH-EN**} & \multicolumn{3}{c}{**JA-EN**} & \multicolumn{3}{c}{**FR-EN**} \\ \cline{2-9}  & **Hits@10** & **MR** & **MRR** & **Hits@10** & **MR** & **MRR** & **Hits@10** & **MR** & **MRR** \\ \hline MTransE & 0.435 & 356.7 & 0.334 & 0.578 & 384.5 & 0.496 & 0.562 & 289.5 & 0.451 \\ IPTransE & 0.559 & 208.2 & 0.508 & 0.452 & 329.6 & 0.384 & 0.441 & 224.3 & 0.343 \\ JAPE & 0.662 & 127.4 & 0.600 & 0.449 & 240.2 & 0.408 & 0.689 & 191.2 & 0.635 \\ BootEA & 0.629 & 109.8 & 0.589 & 0.661 & 109.4 & 0.623 & 0.762 & 117.6 & 0.719 \\ MultiKE & 0.701 & 66.2 & 0.627 & 0.702 & 78.9 & 0.653 & 0.812 & 101.8 & 0.746 \\ KDCoE & 0.668 & 78.0 & 0.611 & 0.652 & 91.1 & 0.611 & 0.817 & 92.5 & 0.802 \\ Our Method & 0.956 & 61.4 & 0.916 & 0.980 & 56.0 & 0.922 & 0.966 & 47.3 & 0.935 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison between our method and other methods in entity alignment performance on the DBP15K dataset, using Hits@10, MR, and MRR. Figure 3: Epoch-wise changes in the F1 measure of our method and Models I-III on: (**a**) ZH-EN; (**b**) JA-EN; (**c**) FR-EN; (**d**) DBP-WD; (**e**) DBP-YG datasets. MTransE, IPTransE, and JAPE are reliant on the number of seed alignments, which constrains their ability to improve the entity alignment performance. Although KDCoE uses entity descriptions for entity alignment, the semantic information in these descriptions has limited applications. Furthermore, KDCoE adds newly labeled entities to the seed set during co-training, making it susceptible to error accumulation. As a result, the entity alignment performance of this model is inferior to that of the proposed model. Furthermore, in comparison to the proposed model to the MultiKE model, which utilizes the entity structure, names, and descriptions for entity alignment, our model exhibited higher Hits@10 and MRR scores and lower MR scores, indicating that our model better uses the entity, relation, and attribute information, and hence, performs better in entity alignment. The experimental results in Tables 6 and 7 are shown as bar charts in Figure 4. The Hits@10, MR, and MRR of the aforementioned methods on the DBP15K and DWY100K datasets are shown in Figure 4a-c. As shown in Figure 4a,c, our method exhibited higher Hits@10 and MRR scores compared to the six other methods. Figure 4b indicates that our method exhibited the lowest MR values. Therefore, our method is superior to all other methods in terms of entity alignment performance. Figure 4 contains bar charts comparing the entity alignment performance of our method with that of the six other methods, in terms of Hits@10, MR, and MRR. Tables 8 and 9 compare our method to other methods in terms of precision, recall, and F1 measure on the DBP15K and DWY100K datasets, respectively. According to the experimental results, on the ZH-EN dataset, our model improved precision, recall, and F1 measure by 1.4-7.5%, 5.1-27.5%, and 6.0-19.6%, respectively.