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RotatQ: Knowledge graph embedding based on quaternion unit
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AI
DOI：10.1016/j.neucom.2025.132413.png)
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Abstract
En 中文
Knowledge graph embedding (KGE) aims to own the ability of automatic prediction for missing entity in knowledge graph, which has become a research hotspot due to the incompleteness of knowledge graph. Although existing KGE models, such as TransE and RotatE, achieve good performance, there are still great difficulties in modelling relation patterns, i.e., symmetry/antisymmetry, inversion, and composition, and inferring side-side complex relations prediction, i.e., 1-1, 1-N, N-1, and N-N. Therefore, motivated by Rodrigues' rotation formula, we propose a novel KGE model, called RotatQ, which models entities and relations as a quaternion and utilizes a unit quaternion to realize the rotation from head entity to tail entity. Besides, to implement the multiple representations of entities in different types, we take type information into consideration with hierarchical type encoders. Due to some properties of quaternion, it has three free degrees to model relation patterns and complex relations simply. To demonstrate the effectiveness of our model RotatQ, we validate our model on four prevalent public databases by comparing with other state-of-the-art models. Experimental results on link prediction tasks show that RotatQ not only has good performance in most metrics, but also has good stability in knowledge graphs.
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