CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations

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

Abstract:

Translation, rotation, and scaling are three commonly used geometric manipulation operations in
image processing. Besides, some of them are successfully used in developing effective knowledge
graph embedding (KGE) models such as TransE and RotatE. Inspired by the synergy, we propose
a new KGE model by leveraging all three operations in this work. Since translation, rotation, and
scaling operations are cascaded to form a compound one, the new model is named CompoundE. By
casting CompoundE in the framework of group theory, we show that quite a few scoring-function-
based KGE models are special cases of CompoundE. CompoundE extends the simple distance-based
relation to relation-dependent compound operations on head and/or tail entities. To demonstrate the
effectiveness of CompoundE, we conduct experiments on three popular KG completion datasets.
Experimental results show that CompoundE consistently achieves the state-of-the-art performance.