We design a network, shown in Figure 2, that maps the vector z s ,r to the entity-relation space, i.e., translating it into (s , r ).
we train an auto-encoder where the encoder is fixed to receive the s and r as one-hot inputs, and calculates z s,r in the same way as the DistMult and ConvE encoders respectively (using trained embeddings).