AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations

Yun-Cheng Wang, Xiou Ge, Bin Wang, C.-C. Jay Kuo

Abstract:

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.