  Knowledge graph embedding (KGE), aiming to embed entities and relations into
low-dimensional vectors, has attracted wide attention recently. However, the
existing research is mainly based on the black-box neural models, which makes
it difficult to interpret the learned representation. In this paper, we
introduce DisenE, an end-to-end framework to learn disentangled knowledge graph
embeddings. Specially, we introduce an attention-based mechanism that enables
the model to explicitly focus on relevant components of entity embeddings
according to a given relation. Furthermore, we introduce two novel regularizers
to encourage each component of the entity representation to independently
reflect an isolated semantic aspect. Experimental results demonstrate that our
proposed DisenE investigates a perspective to address the interpretability of
KGE and is proved to be an effective way to improve the performance of link
prediction tasks.
