Cross-lingual Entity Alignment viaJoint Attribute-Preserving Embedding
(c)
['DBP15KFR-EN', 'DBP15KFR-EN', 'FR→EN  [ITALIC] Hits@1', 'FR→EN  [ITALIC] Hits@10', 'FR→EN  [ITALIC] Hits@50', 'FR→EN  [ITALIC] Mean', 'EN→FR  [ITALIC] Hits@1', 'EN→FR  [ITALIC] Hits@10', 'EN→FR  [ITALIC] Hits@50', 'EN→FR  [ITALIC] Mean']
We used a certain proportion of the gold standards as seed alignment while left the remaining as testing data, i.e., the latent aligned entities to discover. The variation of Hits@k with different proportions will be shown shortly. For relationships and attributes, we simply extracted the property pairs with exactly the same labels, which only account for a small portion of the seed alignment. Besides, JE does not give a mandatory constraint on the length of vectors. Instead, it only minimizes ∥v∥22−1 to restrain vector length and brings adverse effect. For MTransE, it models the structures of KBs in different vector spaces, and information loss happens when learning the translation between vector spaces. We found that involving negative triples in structure embedding reduces the random distribution of entities, and involving attribute embedding as constraint further refines the distribution of entities. The two improvements demonstrate that systematic distribution of entities makes for the cross-lingual entity alignment task.