Model-based Subsampling for Knowledge Graph Completion

arXiv:2309.09296v1 Announce Type: cross
Abstract: 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 …

abstract arxiv cs.ai cs.cl cs.lg current datasets embedding graph however knowledge knowledge graph overfitting queries relations sparsity type