These seed entities, labeled with ground truth rather than user labels, initialize the bootstrapping model, so that participants start off with identical embeddings.
we realize this using large-margin metric learning (Cui et al., 2016;Sohn, 2016), minimizing: where (a, b, c) represents a triplet of entities, such that a and b belong to the same category, while c belongs to a different category, and the function s is the cosine similarity between entity embeddings.