  Knowledge representation learning has received a lot of attention in the past
few years. The success of existing methods heavily relies on the quality of
knowledge graphs. The entities with few triplets tend to be learned with less
expressive power. Fortunately, there are many knowledge graphs constructed from
various sources, the representations of which could contain much information.
We propose an adversarial embedding transfer network ATransN, which transfers
knowledge from one or more teacher knowledge graphs to a target one through an
aligned entity set without explicit data leakage. Specifically, we add soft
constraints on aligned entity pairs and neighbours to the existing knowledge
representation learning methods. To handle the problem of possible distribution
differences between teacher and target knowledge graphs, we introduce an
adversarial adaption module. The discriminator of this module evaluates the
degree of consistency between the embeddings of an aligned entity pair. The
consistency score is then used as the weights of soft constraints. It is not
necessary to acquire the relations and triplets in teacher knowledge graphs
because we only utilize the entity representations. Knowledge graph completion
results show that ATransN achieves better performance against baselines without
transfer on three datasets, CN3l, WK3l, and DWY100k. The ablation study
demonstrates that ATransN can bring steady and consistent improvement in
different settings. The extension of combining other knowledge graph embedding
algorithms and the extension with three teacher graphs display the promising
generalization of the adversarial transfer network.
