RAE (Zhang et al., 2018) further introduces the likelihood that two attribute values co-participate in a common n-ary fact, and adds the corresponding relatedness loss multiplied by a weight factor to the embedding loss of m-TransH.
rAE applies a fully-connected neural network to model the above likelihood.