  Recent advances in Knowledge Graph Embedding (KGE) allow for representing
entities and relations in continuous vector spaces. Some traditional KGE models
leveraging additional type information can improve the representation of
entities which however totally rely on the explicit types or neglect the
diverse type representations specific to various relations. Besides, none of
the existing methods is capable of inferring all the relation patterns of
symmetry, inversion and composition as well as the complex properties of 1-N,
N-1 and N-N relations, simultaneously. To explore the type information for any
KG, we develop a novel KGE framework with Automated Entity TypE Representation
(AutoETER), which learns the latent type embedding of each entity by regarding
each relation as a translation operation between the types of two entities with
a relation-aware projection mechanism. Particularly, our designed automated
type representation learning mechanism is a pluggable module which can be
easily incorporated with any KGE model. Besides, our approach could model and
infer all the relation patterns and complex relations. Experiments on four
datasets demonstrate the superior performance of our model compared to
state-of-the-art baselines on link prediction tasks, and the visualization of
type clustering provides clearly the explanation of type embeddings and
verifies the effectiveness of our model.
