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Article

Dual Quaternion Embeddings for Link Prediction

by 1,†, 2,†, 1,* and 3
1
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China
2
College of Information Science and Technology, Jinan University, Guangzhou 510000, China
3
Data Quality Team, WeChat, Tencent Inc., Guangzhou 510000, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Rafael Valencia-Garcia
Appl. Sci. 2021, 11(12), 5572; https://doi.org/10.3390/app11125572
Received: 13 May 2021 / Revised: 8 June 2021 / Accepted: 10 June 2021 / Published: 16 June 2021
(This article belongs to the Section Computing and Artificial Intelligence)
The applications of knowledge graph have received much attention in the field of artificial intelligence. The quality of knowledge graphs is, however, often influenced by missing facts. To predict the missing facts, various solid transformation based models have been proposed by mapping knowledge graphs into low dimensional spaces. However, most of the existing transformation based approaches ignore that there are multiple relations between two entities, which is common in the real world. In order to address this challenge, we propose a novel approach called DualQuatE that maps entities and relations into a dual quaternion space. Specifically, entities are represented by pure quaternions and relations are modeled based on the combination of rotation and translation from head to tail entities. After that we utilize interactions of different translations and rotations to distinguish various relations between head and tail entities. Experimental results exhibit that the performance of DualQuatE is competitive compared to the existing state-of-the-art models. View Full-Text
Keywords: knowledge graph embedding; link prediction; artificial intelligence knowledge graph embedding; link prediction; artificial intelligence
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MDPI and ACS Style

Gao, L.; Zhu, H.; Zhuo, H.H.; Xu, J. Dual Quaternion Embeddings for Link Prediction. Appl. Sci. 2021, 11, 5572. https://doi.org/10.3390/app11125572

AMA Style

Gao L, Zhu H, Zhuo HH, Xu J. Dual Quaternion Embeddings for Link Prediction. Applied Sciences. 2021; 11(12):5572. https://doi.org/10.3390/app11125572

Chicago/Turabian Style

Gao, Liming, Huiling Zhu, Hankz Hankui Zhuo, and Jin Xu. 2021. "Dual Quaternion Embeddings for Link Prediction" Applied Sciences 11, no. 12: 5572. https://doi.org/10.3390/app11125572

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