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

Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework