LAUSR

The knowledge graph completion (KGC) task aims to predict missing links in knowledge graphs. Recently, several KGC models based on translational distance or semantic matching methods have been proposed and have achieved meaningful results. However, existing models have a significant shortcoming–they cannot train entity embedding when an entity does not appear in the training phase. As a result, such models use randomly initialized embeddings for entities that are unseen in the training phase and cause a critical decrease in performance during the test phase. To solve this problem, we propose a new approach that performs KGC task by utilizing the masked language model (MLM) that is used for a pre-trained language model. Given a triple (head entity, relation, tail entity), we mask the tail entity and consider the head entity and the relation as a context for the tail entity. The model then predicts the masked entity from among all entities. Then, the task is conducted by the same process as an MLM, which predicts a masked token with a given context of tokens. Our experimental results show that the proposed model achieves significantly improved performances when unseen entities appear during the test phase and achieves state-of-the-art performance on the WN18RR dataset.