AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with
  Auxiliary Relations
Yun-Cheng Wang Xiou Ge Bin Wang C. -C. Jay Kuo
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.