One-Shot Relational Learning for Knowledge Graphs
Table 3: Results decomposed over different relations. “⋆” denotes the results with standard training settings and “# Candidates” denotes the size of candidate entity set.
['Relations', '# Candidates', 'MRR GMatching', 'MRR ComplEx', 'Hits@10 GMatching', 'Hits@10 ComplEx']
When testing various models, we observe that the results on different relations are actually of high variance. For reference, we also report the embedding model’s performance under standard training settings where 75% of the triples (instead of only one) are used for training and the rest are used for testing. We can see that relations with smaller candidate sets are generally easier and our model could even perform better than the embedding model trained under standard settings. For some relations such as athleteInjuredHisBodypart, their involved entities have very few connections in KG. It is as expected that one-shot learning on these kinds of relations is quite challenging. Those relations with lots of (>3000) candidates are challenging for all models. Even for embedding model with more training triples, the performance on some relations is still very limited. This suggests that the knowledge graph completion task is still far from being solved.