# Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs
## 4 Experiments
### Results and Discussions  
Table 3 shows the comparison results between CLEM and other entity matching methods. We found that CLEM achieved the best performance on the three sub-data sets. For example, on DBP15K zh-en, CLEM achieved the Hits@1 score 85.41% with 2.6% improvement compared to the second best method. The effectiveness of CLEM partly comes from the integration of the multi-modal entity information. CLEM fuses the multi-modal information from the relation view, attribute view, description view and the image view. In contrast to that, MTransE, JAPE and KDCoE only use a part of the above four views. Moreover, although MultiKE uses multi-view integration models to deal with each kind of entity information equally, but the performance is not as good as ours (details are presented in Figure 3). Instead, CLEM adopts multi-view intact space learning (MISL) to solve the insufficiency problem of each individual view and integrate the multi-modal information from all given views. As for DAEA and EVA, they perform relatively well. EVA uses the additional visual embedding to characterise each entity in the embedding space, while EVA ignores the importance of the text description. Although DAEA integrates multi-aspect information to achieve good results, it neglects the use of entity images to eliminate the ambiguity caused by text relevant modal information. **The Ablation Experiment. ** Table 4 shows the results of the ablation experiment. As expected, the performance of different variants without the corresponding modal information decreases obviously. The results show that each modal information is indispensable and plays an important role in the task of entity matching. In addition, we noticed that CLEM-RAD performed the worst among these variants. This phenomenon indicates that CLEM-RAD lacks the most effective view of the four views, i.e., the image view. The effectiveness of the image view is attributed to the fact that we extracted visual features to eliminate the ambiguity in the relation view, attribute view and text view, which is common in heterogeneous knowledge graphs. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **Batch Size** & **Embedding Dimension** & **Margin** & **Learning Rate** \\ \hline zh-en & 0.005 & 125 & 1 & 256 \\ \hline ja-en & 0.005 & 125 & 1.5 & 256 \\ \hline fr-en & 0.005 & 125 & 1.5 & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The best hyper-parameters for different sub-datasets of DBP15K. Moreover, although we found that the image view is the most effective view, the entity matching results rely on image quality to a certain extent according to our error analysis. Since some images are missing in the DBpedia dataset, we collected images from Google Images which also introduces some noise due to the color, background and brightness differences. Such noisy images do cause a number of errors in the entity matching results. **The Multi-View Integration Experiment. ** To verify the effectiveness of our multi-view representation learning method, we conducted the multi-view integration experiment in terms of Hits@1. Figure 3 shows the results. Compared with other variants, we observed that the Hits@1 of our proposed method increased at least 10.22%. This is because compared with the multi-view integration methods of MultiKE, our applied MISL handled the relevance and complementarity between different modal entity information in the intact space better. The use of the MISL method successfully integrates the multi-modal information from each view to generate the final entity representation, which effectively