  Knowledge graph embedding (KGE) models have been proposed to improve the
performance of knowledge graph reasoning. However, there is a general
phenomenon in most of KGEs, as the training progresses, the symmetric relations
tend to zero vector, if the symmetric triples ratio is high enough in the
dataset. This phenomenon causes subsequent tasks, e.g. link prediction etc., of
symmetric relations to fail. The root cause of the problem is that KGEs do not
utilize the semantic information of symmetric relations. We propose KGE
bi-vector models, which represent the symmetric relations as vector pair,
significantly increasing the processing capability of the symmetry relations.
We generate the benchmark datasets based on FB15k and WN18 by completing the
symmetric relation triples to verify models. The experiment results of our
models clearly affirm the effectiveness and superiority of our models against
baseline.
