  Knowledge graph embedding refers to projecting entities and relations in
knowledge graph into continuous vector spaces. State-of-the-art methods, such
as TransE, TransH, and TransR build embeddings by treating relation as
translation from head entity to tail entity. However, previous models can not
deal with reflexive/one-to-many/many-to-one/many-to-many relations properly, or
lack of scalability and efficiency. Thus, we propose a novel method, flexible
translation, named TransF, to address the above issues. TransF regards relation
as translation between head entity vector and tail entity vector with flexible
magnitude. To evaluate the proposed model, we conduct link prediction and
triple classification on benchmark datasets. Experimental results show that our
method remarkably improve the performance compared with several
state-of-the-art baselines.
