CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations
This work addresses knowledge graph completion for AI applications, presenting a novel method that generalizes existing models but is incremental in nature.
The authors tackled the problem of knowledge graph embedding by proposing CompoundE, a model that integrates translation, rotation, and scaling operations, and demonstrated its effectiveness by achieving state-of-the-art performance on three popular KG completion datasets.
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.