Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion The work described in this paper is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418).
Table 2: One-shot KGC results on WDtext and DBPedia, where bold numbers indicate best results over different models on the same metric.
['Model', 'WDtext Hits@10', 'WDtext Hits@5', 'WDtext Hits@1', 'WDtext MRR', 'DBPtext Hits@10', 'DBPtext Hits@5', 'DBPtext Hits@1', 'DBPtext MRR']
Firstly, we compare our overall framework with baselines by conducting an one-shot KGC experiment, which is the most difficult case in few-shot learning. For WDtext, ConMask is a strong baseline and has a better result on Hits@10 and Hits@5, but it performs worse on Hits@1 and MRR compared to our framework. On the other hand, our overall framework outperforms for a large margin compared to all baselines on DBPtext. Since the main difference between the two datasets are the average length of descriptions, we can observe that our framework has a better performance when dealing with long textual descriptions. Besides, the results of the two ablation baselines are significantly worse than our overall framework, and thus we can see both components play an important role in our framework.