# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
## 3 Results
### Validating the Superiority of Our Method in Entity Alignment
On the JA-EN dataset, our model improved precision, recall, and F1 measure by 3.0-16.8%,  
Figure 4: Comparison between our method and six other methods on the ZH-EN, JA-EN, FR-EN, DBP-WD, and DBP-YG datasets in terms of entity alignment performance: (**a**) Hits@10; (**b**) MR; (**c**) MRR). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**ZH-EN**} & \multicolumn{2}{c}{**JA-EN**} & \multicolumn{2}{c}{**FR-EN**} \\ \cline{2-9}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline MTransE & 0.876 & 0.637 & 0.736 & 0.795 & 0.661 & 0.696 & 0.833 & 0.733 & 0.766 \\ IPTransE & 0.892 & 0.646 & 0.750 & 0.872 & 0.726 & 0.793 & 0.894 & 0.749 & 0.834 \\ JAPE & 0.924 & 0.659 & 0.766 & 0.911 & 0.809 & 0.817 & 0.851 & 0.821 & 0.857 \\ BootEA & 0.928 & 0.803 & 0.862 & 0.921 & 0.815 & 0.857 & 0.921 & 0.847 & 0.861 \\ MultiKE & 0.935 & 0.775 & 0.849 & 0.905 & 0.889 & 0.869 & 0.911 & 0.855 & 0.870 \\ KDCoE & 0.937 & 0.861 & 0.872 & 0.933 & 0.874 & 0.901 & 0.930 & 0.879 & 0.871 \\ Our Method & 0.951 & 0.912 & 0.932 & 0.963 & 0.928 & 0.936 & 0.968 & 0.916 & 0.941 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison between our method and six other methods in terms of entity alignment performance (precision, recall, and F1 measure) on the DBP15K dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**DBP-WD**} & \multicolumn{2}{c}{**DBP-YG**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline MTransE & 0.901 & 0.885 & 0.885 & 0.913 & 0.837 & 0.895 \\ IPTransE & 0.923 & 0.886 & 0.894 & 0.918 & 0.864 & 0.910 \\ JAPE & 0.952 & 0.892 & 0.919 & 0.933 & 0.910 & 0.922 \\ BootEA & 0.955 & 0.907 & 0.933 & 0.955 & 0.937 & 0.954 \\ MultiKE & 0.967 & 0.921 & 0.934 & 0.968 & 0.939 & 0.964 \\ KDCoE & 0.977 & 0.934 & 0.952 & 0.977 & 0.945 & 0.967 \\ Our Method & 0.998 & 0.956 & 0.988 & 0.987 & 0.962 & 0.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison between our method and six other methods in terms of entity alignment performance (precision, recall, and F1 measure) on the DWY100K dataset. 3.9-26.7%, and 3.5-24.0%, respectively. On the FR-EN dataset, our model improved precision, recall, and F1 measure by 3.8-13.5%, 3.7-18.3%, and 7.0-17.5%, respectively. On the DBP-WD dataset, our model improved precision, recall, and F1 measure by 2.1-9.7%, 2.2-7.1%, and 3.6-10.3%, respectively. On the DBP-YG dataset, our model improved precision, recall, and F1 measure by 1.0-7.4%, 1.7-12.5%, and 2.4-9.6%, respectively. Therefore, our model significantly improved all three metrics on all datasets, thereby confirming that our model is superior to the other entity alignment models in terms of entity alignment performance. In Figure 5, the data in Tables 8 and 9 are shown in the form of bar charts. Figure 5a-e compare our method to six other methods in terms of precision, recall, and F1 measure on the ZH-EN, JA-EN, FR-EN, DBP-WD, and DBP-YG datasets. These figures clearly indicate that our method exhibited higher precision, recall, and F1 measure scores compared to all other methods, suggesting that our method is superior in terms of entity alignment performance. Figure 5: Comparison between our method and six other methods in terms of entity alignment performance (precision, recall, and F1 measure) on datasets: (**a**) ZH-EN; (**b**) JA-EN; (**c**) FR-EN; (**d**) DBP-WD; (**e**) DBP-YG.