Knowledge Graph Entity Typing with Contrastive Learning

Knowledge graph entity typing is an important way to complete knowledge graphs (KGs), aims at predicting the associating types of certain given entities. However, previous methods suppose that many (entity, entity type) pairs can be obtained for each entity type, performing poorly on entity types that only have a few associative entities. Besides, these methods cannot fully exploit the inherent correlation and complementarity information across different entities sharing the same entity type. To this end, we propose a novel model named Contrastive Entity Typing (CET) for KG entity tying. CET can better learn the mutual interactions among the entities with the same entity type and can fully utilize the hierarchical information in entity type trees by two contrastive learning modules. The main benefit of the proposed contrastive learning modules is that they can effectively encourage the consistency of the entity representations with the same type while improving the discriminability of the entity type classifiers. Empirically, our model achieves state-of-the-art results on KG entity typing benchmarks.