Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
Table 2: The overall alignment performance for all models on the DBP15K datasets. Numbers in bold indicate the best performance.
['[BOLD] Models', 'ZH-EN Hits@1', 'ZH-EN Hits@10', 'JA-EN Hits@1', 'JA-EN Hits@10', 'FR-EN Hits@1', 'FR-EN Hits@10']
By using a bootstrapping process to iteratively explore many unlabeled data, BootEA gives the best Hits@10 score on DBP15KZH−EN and clearly outperforms GCN and other translation-based models. It is not surprising that GCN outperforms most translation-based models, i.e., JE, MTransE, JAPE and IPTransE. By performing graph convolution over an entity’s neighbors, GCN is able to capture more structural characteristics of knowledge graphs, especially when using more GCN layers, while the translation assumption in translation-based models focuses more on the relationship among heads, tails and relations.