For efficiency, we use negative sampling to reformulate the training objective, which is then optimized through coordinate gradient ascent.
given the training data {(e T , e C )} extracted from KB corpora, the representation learning is formulated as maximizing the objective in Eq 1, subject to PSD constraints on distance metrics M h 0, and v e 2 = v e 2 = 1 to avoid scale ambiguity.