We compare the performance of our method against state-of-the-art representation learning baselines that use neural embedding techniques to learn entity and relation representation.
we consider compositional methods of RESCAL (Nickel et al., 2011) as basic matrix factorization method, DISTMULT (Yang et al., 2015) as simple multiplicative model good for capturing symmetric relationships, and Complex (Trouillon et al., 2016), an upgrade over DISTMULT that can capture asymmetric relationships using complex valued embeddings.