Instead, we use its center embedding to transfer information among different relations and the sum of the center embedding and a local embedding to represent the final embedding for the corresponding relation 2 .
moreover, considering there are various relations among words, we use a lower dimension s for storage compression, with s < d. a transformation matrix X is introduced to transform u into the same vector space of c. As a result, the final embeddings v for head and tail of w under relation r are formulated as: (1) To train v r h,w and v r t,w for each w under r, we adopt the negative sampling strategy to conduct the learning process.