Papers › Knowledge Graph Embedding with 3D Compound Geometric Transformations
Knowledge Graph Embedding with 3D Compound Geometric Transformations
Xiou Ge, Yun-Cheng Wang, Bin Wang, C. -C. Jay Kuo
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Property Prediction | ogbl-wikikg2 | CompoundE3D | Ext. data | No | #7 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | CompoundE3D | Number of params | 750662700 | #7 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | CompoundE3D | Test MRR | 0.7006 ± 0.0011 | #7 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | CompoundE3D | Validation MRR | 0.7175 ± 0.0015 | #7 of 30 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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