Knowledge graph entity typing (KGET) is a task to predict the missing entity types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to solve the KGET task by introducing an auxiliary relation, 'hasType', to model the relationship between entities and their types. However, a single auxiliary relation has limited expressiveness for diverse entity-type patterns. We improve the expressiveness of KGE methods by introducing multiple auxiliary relations in this work. Similar entity types are grouped to reduce the number of auxiliary relations and improve their capability to model entity-type patterns with different granularities. With the presence of multiple auxiliary relations, we propose a method adopting an Asynchronous learning scheme for Entity Typing, named AsyncET, which updates the entity and type embeddings alternatively to keep the learned entity embedding up-to-date and informative for entity type prediction. Experiments are conducted on two commonly used KGET datasets to show that the performance of KGE methods on the KGET task can be substantially improved by the proposed multiple auxiliary relations and asynchronous embedding learning. Furthermore, our method has a significant advantage over state-of-the-art methods in model sizes and time complexity.

翻译：知识图谱实体类型识别（KGET）是一项预测知识图谱（KG）中缺失实体类型的任务。以往的知识图谱嵌入（KGE）方法通过引入辅助关系“hasType”来建模实体与类型之间的关系，以解决KGET任务。然而，单一的辅助关系对于多样化的实体-类型模式的表达能力有限。本文通过引入多个辅助关系来提升KGE方法的表达能力。将相似实体类型分组以减少辅助关系的数量，并增强其建模不同粒度实体-类型模式的能力。在多个辅助关系存在的情况下，我们提出了一种采用异步学习方案进行实体类型识别的方法，命名为AsyncET，该方法交替更新实体和类型嵌入，使学习到的实体嵌入保持最新且信息丰富，从而用于实体类型预测。在两个常用的KGET数据集上进行的实验表明，通过所提出的多辅助关系和异步嵌入学习，KGE方法在KGET任务上的性能可以得到显著提升。此外，我们的方法在模型大小和时间复杂度方面相较于现有最优方法具有显著优势。