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Pre-trained language models (LMs) like BERT have shown to store factualknowledge about the world. This knowledge can be used to augment theinformation present in Knowledge Bases, which tend to be incomplete. However,prior attempts at using BERT for task of Knowledge Base Completion (KBC)resulted in performance worse than embedding based techniques that rely only onthe graph structure. In this work we develop a novel model, Cross-Entity AwareReranker (CEAR), that uses BERT to re-rank the output of existing KBC modelswith cross-entity attention. Unlike prior work that scores each entityindependently, CEAR uses BERT to score the entities together, which iseffective for exploiting its factual knowledge. CEAR achieves a new state ofart for the OLPBench dataset.

CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion