Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs. Existing approaches to KGET focus on how to better encode the knowledge provided by the neighbors and types of an entity into its representation. However, they ignore the semantic knowledge provided by the way in which types can be clustered together. In this paper, we propose a novel method called Multi-view Contrastive Learning for knowledge graph Entity Typing (MCLET), which effectively encodes the coarse-grained knowledge provided by clusters into entity and type embeddings. MCLET is composed of three modules: i) Multi-view Generation and Encoder module, which encodes structured information from entity-type, entity-cluster and cluster-type views; ii) Cross-view Contrastive Learning module, which encourages different views to collaboratively improve view-specific representations of entities and types; iii) Entity Typing Prediction module, which integrates multi-head attention and a Mixture-of-Experts strategy to infer missing entity types. Extensive experiments show the strong performance of MCLET compared to the state-of-the-art

翻译：知识图谱实体类型推断（KGET）旨在推断知识图谱中实体的可能类型。现有KGET方法侧重于如何更好地将实体的邻居和类型所提供的知识编码到其表示中，却忽略了类型聚类方式所蕴含的语义知识。本文提出一种名为多视角对比学习知识图谱实体类型推断（MCLET）的新方法，它有效将聚类提供的粗粒度知识编码到实体和类型嵌入中。MCLET包含三个模块：（i）多视角生成与编码模块，该模块对来自实体-类型、实体-聚类和聚类-类型视角的结构化信息进行编码；（ii）跨视角对比学习模块，该模块鼓励不同视角协同提升实体和类型在各自视角下的特定表示；（iii）实体类型预测模块，该模块整合多头注意力机制与专家混合策略来推断缺失的实体类型。大量实验表明，与当前最先进方法相比，MCLET展现出卓越性能。