# Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations
## 3 Results
### Validation of the Proposed Entity Alignment Method  
In this section, we investigated the effect of (1) including semantic information from multi-step relation paths in structural embedding learning (Section 3.2) and (2) using improved loss function for embedding learning (Section 3.2) on the entity alignment performance of the proposed model. Furthermore, the proposed method will be compared to the following models to verify its effectiveness in improving entity alignment performance. Model I: The semantic information in multi-step relation paths is not considered when learning structural embeddings, and the conventional loss function (margin-based ranking criterion) is used for embedding learning. Model II: The semantic information in multi-step relation paths is considered when learning structural embeddings, and the conventional loss function (margin-based ranking criterion) is used for embedding learning. Model III: The semantic information in multi-step relation paths is not considered when learning structural embeddings, and the loss function proposed in Section 3.2 (limit-based loss function) is used for embedding learning. Tables 2 and 3 show the entity alignment performances of the proposed method and Models I, II, and II on the DBP15K and DWY100K datasets, in terms of Hits@10, MR, and MRR. As shown in Tables 2 and 3, the proposed method outperforms Models I-III by varying degrees on the DBP15K and DWY100K datasets in all metrics. Furthermore, models II and III outperform Model I on both datasets. Therefore, including semantic information from multi-step relation paths when learning structural embeddings and using the improved loss function can benefit the entity alignment performance, thereby proving the efficacy of the proposed method. Figure 1 presents the data in Tables 2 and 3 as bar charts to provide a better understanding of the experimental results. Figure 1a-c compare the proposed method to six other methods in terms of Hits@10, MR, and MRR on the DBP15K and DWY100K datasets. As shown in Figure 1a,c, the proposed method exhibited significantly higher Hits@10 and MRR scores compared to models I, II, and III. In addition, Figure 1b indicates that our method exhibited a significantly lower MR than models I, II, and III, which provides further evidence of the efficacy of our method in improving entity alignment performance. Tables 4 and 5 show the entity alignment performances of our method and Models I-III in terms of precision, recall, and F1 measure on the DBP15K and DWY100K datasets. According to the experimental results, on the ZH-EN dataset, the proposed method improved precision, recall, and F1 measure by 3.6-8.5%, 2.5-10.5%, and 2.2-9.8%, respectively, compared to the ZH-EN dataset. On the JA-EN dataset, our method improved precision, recall, and F1 measure by 4.6-12.8%, 5.1-10.9%, and 2.7-11.5%, respectively. On the FR-EN dataset, our method improved precision, recall, and F1 measure by 5.7-13.1%, 3.4-11.3%, and 1.8-9.9%, respectively. On the DBP-WD dataset, our method improved precision, recall, and F1 measure by 5.4-12.2%, 3.4-7.5%, and 3.2-10.6%, respectively. On the DBP-YG dataset, our method improved precision, recall, and F1 measure by 4.0-13.4%, 8.3-14.6%, and 4.0-10.9%, respectively. These results indicate that our model improves the performance of all aforementioned datasets in all metrics, and hence, outperforms Models I-III. The data in Tables 4 and 5 are shown as bar charts in Figure 2 to facilitate a more intuitive understanding of the experimental results. Figure 2a-e compare our method to Models I-III on the ZH-EN, JA-EN, FR-EN, DBP-WD, and DBP-YG datasets, in terms of precision, recall, and F1 measure. The comparison results verify that our method significantly outperforms Models I-III on all three metrics, highlighting the effectiveness of our model in improving entity alignment performance.