Model-based Subsampling for Knowledge Graph Completion
Xincan Feng Hidetaka Kamigaito Katsuhiko Hayashi Taro Watanabe
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing
overfitting caused by the sparsity in Knowledge Graph (KG) datasets. However,
current subsampling approaches consider only frequencies of queries that
consist of entities and their relations. Thus, the existing subsampling
potentially underestimates the appearance probabilities of infrequent queries
even if the frequencies of their entities or relations are high. To address
this problem, we propose Model-based Subsampling (MBS) and Mixed Subsampling
(MIX) to estimate their appearance probabilities through predictions of KGE
models. Evaluation results on datasets FB15k-237, WN18RR, and YAGO3-10 showed
that our proposed subsampling methods actually improved the KG completion
performances for popular KGE models, RotatE, TransE, HAKE, ComplEx, and
DistMult.