Is Aligning Embedding Spaces a Challenging Task? A Study on Heterogeneous Embedding Alignment Methods
Table 2: Entity Alignment Task
['Models', 'DBP-LGD Hits@1', 'DBP-LGD Hits@10', 'DBP-LGD MR', 'DBP-GEO Hits@1', 'DBP-GEO Hits@10', 'DBP-GEO MR', 'DBP-YAGO Hits@1', 'DBP-YAGO Hits@10', 'DBP-YAGO MR']
Intuitively, it is easier to align entities with more content. The attribute information plays a vital role in the alignment task as it captures more semantics of the entities in a KG. Hence, N−gramattr and BootEA perform better than the other models. Also, it has been noticed that more the number of aligned seeds the better the performance of the model. The main advantage of the GCN model is that huge number of aligned seeds are not required for training.