Papers › Knowledge Graph Embedding with 3D Compound Geometric Transformations

Knowledge Graph Embedding with 3D Compound Geometric Transformations

1 Apr 2023arXiv:2304.00378archive 2025-07-28

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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hughxiouge/CompoundE3D mentioned on GitHubpytorch report

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Tasks

Graph EmbeddingKnowledge Graph EmbeddingLink PredictionTranslation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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