To discover inference rules, we first compute similarities between predicates, then two predicates p and q in context c will form an inference rule if their similarity is above a threshold.
because each representation ⃗ can be viewed as a distribution over nodes, we measure the similarity between two predicates using the Kullback-Leibler divergence between ⃗ and ⃗ (Kullback & Leibler, 1951): Notice that KL divergence is a distance measure: the smaller the KL divergence between ⃗ and ⃗ , the more similar the two predicates p and q.