KSE is an separately pre-trained model to embed the relations and types in KB in an Euclidean space, by using an adaptation of the TransH (Wang et al., 2014) to the "schema graph", which is a graphical representation of the KB schema set S of all "schema triples" (t, r, t ) ∈ T × R × T for which knowledge triples (s, r, o) with t(s) = t and t(o) = t are allowed.
it is worth noting that to properly define the verification loss ve , we need to introduce "negative examples" for CLS.To that end, denote D qsr is the set of all training examples for path label prediction (ENC-DEC).