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arXiv · 2304.00378

Knowledge Graph Embedding with 3D Compound Geometric Transformations

Xiou Ge·Yun-Cheng Wang·Bin Wang·C. -C. Jay Kuo
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Abstract

The cascade of 2D geometric transformations were exploited to model relations between entities in a knowledge graph (KG), leading to an effective KG embedding (KGE) model, CompoundE. Furthermore, the rotation in the 3D space was proposed as a new KGE model, Rotate3D, by leveraging its non-commutative property. Inspired by CompoundE and Rotate3D, we leverage 3D compound geometric transformations, including translation, rotation, scaling, reflection, and shear and propose a family of KGE models, named CompoundE3D, in this work. CompoundE3D allows multiple design variants to match rich underlying characteristics of a KG. Since each variant has its own advantages on a subset of relations, an ensemble of multiple variants can yield superior performance. The effectiveness and flexibility of CompoundE3D are experimentally verified on four popular link prediction datasets.

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Xiou Ge, Yun-Cheng Wang, Bin Wang, C. -C. Jay Kuo. 2023-04-01. Knowledge Graph Embedding with 3D Compound Geometric Transformations. https://arxiv.org/abs/2304.00378

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