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 3: Four-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']
In real-world KGs, few-shot learning of infrequent relations and uncommon entities are more common than one-shot scenario, so we also conduct four-shot KGC as another experiment. We use the same baselines and metrics as that in the one-shot KGC experiment. By comparing our overall framework with two ablation baselines, the importance of traits and triplet augmentation is demonstrated again. Furthermore, when compared with the previous one-shot KGC results, all baselines in this experiment are able to make use of the extra training data to improve their performances. Our framework also performs better on all metrics in WDtext, but it performs inferior on some metrics in DBPtext when compared with the one-shot scenario. One possible reason is descriptions in DBPtext is longer and more complex than that in WDtext, and thus four descriptions of training triplets are too diverse to learn a good representation.