  Knowledge Graphs (KGs) are fundamental resources in knowledge-intensive tasks
in NLP. Due to the limitation of manually creating KGs, KG Completion (KGC) has
an important role in automatically completing KGs by scoring their links with
KG Embedding (KGE). To handle many entities in training, KGE relies on Negative
Sampling (NS) loss that can reduce the computational cost by sampling. Since
the appearance frequencies for each link are at most one in KGs, sparsity is an
essential and inevitable problem. The NS loss is no exception. As a solution,
the NS loss in KGE relies on smoothing methods like Self-Adversarial Negative
Sampling (SANS) and subsampling. However, it is uncertain what kind of
smoothing method is suitable for this purpose due to the lack of theoretical
understanding. This paper provides theoretical interpretations of the smoothing
methods for the NS loss in KGE and induces a new NS loss, Triplet Adaptive
Negative Sampling (TANS), that can cover the characteristics of the
conventional smoothing methods. Experimental results of TransE, DistMult,
ComplEx, RotatE, HAKE, and HousE on FB15k-237, WN18RR, and YAGO3-10 datasets
and their sparser subsets show the soundness of our interpretation and
performance improvement by our TANS.
