DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention
  Network
  Knowledge graph completion (KGC) has become a focus of attention across deep
learning community owing to its excellent contribution to numerous downstream
tasks. Although recently have witnessed a surge of work on KGC, they are still
insufficient to accurately capture complex relations, since they adopt the
single and static representations. In this work, we propose a novel
Disentangled Knowledge Graph Attention Network (DisenKGAT) for KGC, which
leverages both micro-disentanglement and macro-disentanglement to exploit
representations behind Knowledge graphs (KGs). To achieve
micro-disentanglement, we put forward a novel relation-aware aggregation to
learn diverse component representation. For macro-disentanglement, we leverage
mutual information as a regularization to enhance independence. With the
assistance of disentanglement, our model is able to generate adaptive
representations in terms of the given scenario. Besides, our work has strong
robustness and flexibility to adapt to various score functions. Extensive
experiments on public benchmark datasets have been conducted to validate the
superiority of DisenKGAT over existing methods in terms of both accuracy and
explainability.
