Similar to previous work (Riedel et al., 2010;Zeng et al., 2015), we use two settings for evaluation: (1) Held-out evaluation, where a subset of relational facts in KB is held out from training (Table 1), and is later used to compare against newly discovered rela- tional facts.
the results in Figure 6 show that pairwise ensemble of existing relation extraction models does not yield much improvement, and GloRE brings much larger improvement than the other models.