  Many large-scale knowledge graphs are now available and ready to provide
semantically structured information that is regarded as an important resource
for question answering and decision support tasks. However, they are built on
rigid symbolic frameworks which makes them hard to be used in other intelligent
systems. We present a learning method using generative adversarial architecture
designed to embed the entities and relations of the knowledge graphs into a
continuous vector space. A generative network (GN) takes two elements of a
(subject, predicate, object) triple as input and generates the vector
representation of the missing element. A discriminative network (DN) scores a
triple to distinguish a positive triple from those generated by GN. The
training goal for GN is to deceive DN to make wrong classification. When
arriving at a convergence, GN recovers the training data and can be used for
knowledge graph completion, while DN is trained to be a good triple classifier.
Unlike few previous studies based on generative adversarial architectures, our
GN is able to generate unseen instances while they just use GN to better choose
negative samples (already existed) for DN. Experiments demonstrate our method
can improve classical relational learning models (e.g.TransE) with a
significant margin on both the link prediction and triple classification tasks.
