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 4: MRR results of our framework when using different number of augmentation in TCVAE, where #Gen means the number of triplets being generated.
['WDtext #Gen', 'WDtext MRR', 'DBPtext #Gen', 'DBPtext MRR']
As shown in the previous KGC experiments, the performance of our framework heavily depends on the triplet generation provided by TCVAE. In this subsection, we further explore the effect of triplet generation by comparing the MRR result of our overall framework with different number of generated triplets in one-shot scenario, where the triplet augmentation is particularly helpful. For this analysis, we only conduct experiment and report the result with one trial for simplicity. Besides, the appropriate number of triplets being generated varies from one dataset to another, and too many or too few generation leads to an inferior performance. In WDtext, generating 8 extra triplets enhances the performance most, but generating 128 triplets is better in DBPtext. One reason for such a difference is that DBPtext has longer descriptions which also increase the variance of the generated triplets, and thus generating more triplets is necessary to learn a stable representation.