1 Introduction
Table 7: Hyperparameters for training TransE knowledge graph embeddings. Bolded values indicate we performed a grid search. Other values are from Bordes et al. (2013) and Han et al. (2018).
['Hyperparameter', 'Value']
We use the OpenKE repository to generate knowledge graph embeddings Han et al. We follow the training hyperparameters described in Bordes et al. We modify the repository to follow the early stopping procedure and normalization of entity embeddings to follow the protocol of Bordes et al. We additionally sweep the learning rate in {1e-5, 0.0001, 0.001, 0.01, 0.1} using dimension 50 on the FB15K-95 dataset, and choose the learning rate which attains the lowest mean rank (i.e., highest quality) on the validation set for the link prediction task. We also note that unlike with word embeddings, we do not align embeddings with orthogonal Procrustes before compressing the embeddings with uniform quantization. We found alignment to result in a quality drop on knowledge graph embeddings, likely due to the fact that there are two sets of embeddings jointly learned (relation and entity embeddings) which require more advanced alignment techniques.