Large-scale knowledge graphs (KGs) are shown to become more important in
current information systems. To expand the coverage of KGs, previous studies on
knowledge graph completion need to collect adequate training instances for
newly-added relations. In this paper, we consider a novel formulation,
zero-shot learning, to free this cumbersome curation. For newly-added
relations, we attempt to learn their semantic features from their text
descriptions and hence recognize the facts of unseen relations with no examples
being seen. For this purpose, we leverage Generative Adversarial Networks
(GANs) to establish the connection between text and knowledge graph domain: The
generator learns to generate the reasonable relation embeddings merely with
noisy text descriptions. Under this setting, zero-shot learning is naturally
converted to a traditional supervised classification task. Empirically, our
method is model-agnostic that could be potentially applied to any version of KG
embeddings, and consistently yields performance improvements on NELL and Wiki
dataset