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    <abstract>Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet either through semantic matching or distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with entity‘s geometric distance to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on the head entity and a reverse rotation on the tail entity to learn the rich semantic features. Then, we utilize distance adaptive translations to learn the geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://anonymous.4open.science/r/l2730.</abstract>
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%0 Conference Proceedings
%T Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
%A Wang, Weihua
%A Liang, Qiuyu
%A Bao, Feilong
%A Gao, Guanglai
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-distance
%X Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet either through semantic matching or distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with entity‘s geometric distance to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on the head entity and a reverse rotation on the tail entity to learn the rich semantic features. Then, we utilize distance adaptive translations to learn the geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://anonymous.4open.science/r/l2730.
%U https://aclanthology.org/2025.coling-main.284/
%P 4219-4231

Markdown (Informal)

[Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation](https://aclanthology.org/2025.coling-main.284/) (Wang et al., COLING 2025)

• Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation (Wang et al., COLING 2025)

ACL

• Weihua Wang, Qiuyu Liang, Feilong Bao, and Guanglai Gao. 2025. Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 4219–4231, Abu Dhabi, UAE. Association for Computational Linguistics.