  This paper studies aligning knowledge graphs from different sources or
languages. Most existing methods train supervised methods for the alignment,
which usually require a large number of aligned knowledge triplets. However,
such a large number of aligned knowledge triplets may not be available or are
expensive to obtain in many domains. Therefore, in this paper we propose to
study aligning knowledge graphs in fully-unsupervised or weakly-supervised
fashion, i.e., without or with only a few aligned triplets. We propose an
unsupervised framework to align the entity and relation embddings of different
knowledge graphs with an adversarial learning framework. Moreover, a
regularization term which maximizes the mutual information between the
embeddings of different knowledge graphs is used to mitigate the problem of
mode collapse when learning the alignment functions. Such a framework can be
further seamlessly integrated with existing supervised methods by utilizing a
limited number of aligned triples as guidance. Experimental results on multiple
datasets prove the effectiveness of our proposed approach in both the
unsupervised and the weakly-supervised settings.
