Jointly Learning Entity and Relation Representations for Entity Alignment
Table 2: Performance on entity alignment.
['[BOLD] Models', '[BOLD] ZH-EN  [BOLD] Hits@1', '[BOLD] ZH-EN  [BOLD] Hits@10', '[BOLD] JA-EN  [BOLD] Hits@1', '[BOLD] JA-EN  [BOLD] Hits@10', '[BOLD] FR-EN  [BOLD] Hits@1', '[BOLD] FR-EN  [BOLD] Hits@10']
The top part of the table shows the performance of prior approaches. By using a bootstrapping process to expand the training data, BootEA clearly outperforms all prior methods. By capturing the rich neighboring structural information, GCN outperforms all other translation-based models on Hits@1, and over IPTransE, MTransE and JE on Hits@10. After initialized with the machine-translated entity names, GCN-PE considerably improves GCN on all datasets. By employing layer-wise highway gates, HGCN-PE further improves GCN-PE, giving a 34.31% improvement on Hits@1 on DBP15KFR−EN, and also outperforms the strongest baseline BootEA. This substantial improvement indicates that highway gates can effectively control the propagation of noisy information. Our complete framework HGCN-JE gives the best performance across all metrics and datasets. Comparing HGCN-JE with HGCN-PE and GCN-JE with GCN-PE (2.36% and 4.19% improvements of Hits@1 on DBP15KZH−EN respectively), we observe that joining entity and relation alignments improves the model performance. Even without entity name initialization, GCN-JE-r still has obvious advantages over JE, MTransE, JAPE, IPTransE and GCN. The results reinforce our claim that merging the relation information into entities can produce better entity representations. We stress that our proposed methods are not restricted to GCNs or HGCNs, but can be flexibly integrated with other KG representation models as well.