  Research on knowledge graph embedding (KGE) has emerged as an active field in
which most existing KGE approaches mainly focus on static structural data and
ignore the influence of temporal variation involved in time-aware triples. In
order to deal with this issue, several temporal knowledge graph embedding
(TKGE) approaches have been proposed to integrate temporal and structural
information in recent years. However, these methods only employ a uniformly
random sampling to construct negative facts. As a consequence, the corrupted
samples are often too simplistic for training an effective model. In this
paper, we propose a new temporal knowledge graph embedding framework by
introducing adversarial learning to further refine the performance of
traditional TKGE models. In our framework, a generator is utilized to construct
high-quality plausible quadruples and a discriminator learns to obtain the
embeddings of entities and relations based on both positive and negative
samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the
Wasserstein distance to prevent vanishing gradient problems on discrete data;
an inherent flaw in traditional generative adversarial networks. Through
comprehensive experimentation on temporal datasets, the results indicate that
our proposed framework can attain significant improvements based on benchmark
models and also demonstrate the effectiveness and applicability of our
framework.
