  Knowledge Graph (KG) completion research usually focuses on densely connected
benchmark datasets that are not representative of real KGs. We curate two KG
datasets that include biomedical and encyclopedic knowledge and use an existing
commonsense KG dataset to explore KG completion in the more realistic setting
where dense connectivity is not guaranteed. We develop a deep convolutional
network that utilizes textual entity representations and demonstrate that our
model outperforms recent KG completion methods in this challenging setting. We
find that our model's performance improvements stem primarily from its
robustness to sparsity. We then distill the knowledge from the convolutional
network into a student network that re-ranks promising candidate entities. This
re-ranking stage leads to further improvements in performance and demonstrates
the effectiveness of entity re-ranking for KG completion.
