For each minibatch of positive triples, a small number of negative triples are sampled by corrupting head or tail for each positive triple, then related parameters in the model are updated by minimizing the binary negative log-likelihood such that positive triples will get higher scores than negative triples.
the loss function is written as follows, where λ ∈ R is the L 2 regularization coefficient for entity embeddings only, T + and T − are the sets of positive and sampled negative triples in a minibatch, and y equals to 1 if (h, r, t) ∈ T + otherwise −1.