Jointly Learning Entity and Relation Representations for Entity Alignment
Table 3: Performance on relation 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']
Directly using the relation embeddings learned by MTransE to perform relation alignment leads to rather poor performance for MTransE-R, less than 4% for Hits@1 for all datasets. This is because the translation assumption, head+relation≈tail, used by MTransE focuses on modeling the overall relationship among heads, tails, and relations, but capturing little neighboring information and relation semantics. This confirms our assumption that it is feasible to approximate a relation using the information of its head and tail entities.