We address these questions by analyses showing that (i) the autoencoder learns sparse and interpretable codings of relations, (ii) the joint training drives relation matrices toward a low dimension manifold, and (iii) it helps discovering compositional constraints.
on WN18RR and FB15k-237, Dettmers et al.