A GPT-assisted Multi-Granularity Contrastive Learning approach for Knowledge Graph Entity Typing

This is the authors' abstract. We don't add key points for this paper.

Knowledge graph entity typing (KGET) is an efficient way to infer possible missing types for entities, which has become a key instrument to enhance the construction of knowledge graphs (KGs). Existing models to KGET have mainly focused on a single granularity information such as distinct entity information, but other granularity information including entity-to-type-clusters, the same cluster and interaction information have not been fully explored, resulting in inferring incorrect types in KGs. To address this, we propose a GPT-assisted Multi-Granularity Contrastive Learning (GMGCL) approach to acquire entity-to-type-clusters, entity, type-cluster and relation information by GPT-assisted entity-to-type-clusters clustering, entity-based, cluster-based and relation-based contrastive learning, respectively. Our approach is evaluated on FB15kET and YAGO43kET datasets, outperforming other baselines and obtaining a 1.35% average improvement at least on MRR.