  Knowledge representation learning aims at modeling knowledge graph by
encoding entities and relations into a low dimensional space. Most of the
traditional works for knowledge embedding need negative sampling to minimize a
margin-based ranking loss. However, those works construct negative samples
through a random mode, by which the samples are often too trivial to fit the
model efficiently. In this paper, we propose a novel knowledge representation
learning framework based on Generative Adversarial Networks (GAN). In this
GAN-based framework, we take advantage of a generator to obtain high-quality
negative samples. Meanwhile, the discriminator in GAN learns the embeddings of
the entities and relations in knowledge graph. Thus, we can incorporate the
proposed GAN-based framework into various traditional models to improve the
ability of knowledge representation learning. Experimental results show that
our proposed GAN-based framework outperforms baselines on triplets
classification and link prediction tasks.
