Training and Inference To train the model, we randomly construct 20 negative examples for each positive example <e 1 i , e 2 j > using a heuristic method.
we first generate rough entity embeddings for G 1 and G 2 by summing over the pretrained embeddings of words within each entity's surface form; then, we select 10 closest entities to e 1 i (or e 2 j ) in the rough embedding space to construct negative pairs with e 2 j (or e 1 i ).