  A fundamental task for knowledge graphs (KGs) is knowledge graph completion
(KGC). It aims to predict unseen edges by learning representations for all the
entities and relations in a KG. A common concern when learning representations
on traditional graphs is degree bias. It can affect graph algorithms by
learning poor representations for lower-degree nodes, often leading to low
performance on such nodes. However, there has been limited research on whether
there exists degree bias for embedding-based KGC and how such bias affects the
performance of KGC. In this paper, we validate the existence of degree bias in
embedding-based KGC and identify the key factor to degree bias. We then
introduce a novel data augmentation method, KG-Mixup, to generate synthetic
triples to mitigate such bias. Extensive experiments have demonstrated that our
method can improve various embedding-based KGC methods and outperform other
methods tackling the bias problem on multiple benchmark datasets.

Machine Learning