Papers › TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
Jiang Li, Xiangdong Su, Fujun Zhang, Guanglai Gao
This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at~\url{https://github.com/dellixx/TransERR}.
Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Property Prediction | ogbl-wikikg2 | TransERR | Ext. data | No | #16 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | TransERR | Number of params | 500441802 | #16 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | TransERR | Test MRR | 0.6359 ± 0.0020 | #16 of 30 | Archive leaderboard | report |
| Link Property Prediction | ogbl-wikikg2 | TransERR | Validation MRR | 0.6518 ± 0.0012 | #16 of 30 | Archive leaderboard | report |
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