Most of these approaches use word-level embeddings to encode entities and predicates, and therefore might suffer from the out-of-vocabulary (OOV) problem when they encounter unseen words during test time.
they often rely on significant data augmentation from sources such as Paralex , which contains 18 million question-paraphrase pairs scraped from WikiAnswers, to have sufficient examples for each word they encounter (Bordes et al., 2014b;Yih et al., 2014;Bordes et al., 2015).