用双曲空间直接建模知识图谱关系，提升层次结构表示能力。
Fully Hyperbolic Rotation for Knowledge Graph Embedding
- 在Lorentz模型中直接定义关系为双曲旋转，避免映射损失。
- 在CoDEx-s/m数据集上达到最新最优性能，参数更少。
- 适合需要高效表达层次化知识的场景，如语义推理与推荐系统。
双曲旋转常用于有效建模知识图谱及其固有层次结构。然而，现有双曲旋转模型依赖对数与指数映射进行特征变换，仅将数据特征投影至双曲空间进行旋转，限制了对双曲空间的充分挖掘。为此，我们提出一种全新的全双曲模型，专用于知识图谱嵌入。该模型不依赖特征映射，而是直接在双曲空间中以Lorentz模型定义。我们将知识图谱中的每条关系视为从头实体到尾实体的Lorentz旋转，并采用Lorentz距离作为评分函数衡量三元组合理性。在标准知识图谱补全基准上的大量实验表明，本模型以更少参数获得具有竞争力的结果；尤其在更具多样性和挑战性的CoDEx-s和CoDEx-m数据集上，取得当前最优性能。代码已公开于https://github.com/llqy123/FHRE。
原文摘要 · Abstract (English)
Hyperbolic rotation is commonly used to effectively model knowledge graphs and their inherent hierarchies. However, existing hyperbolic rotation models rely on logarithmic and exponential mappings for feature transformation. These models only project data features into hyperbolic space for rotation, limiting their ability to fully exploit the hyperbolic space. To address this problem, we propose a novel fully hyperbolic model designed for knowledge graph embedding. Instead of feature mappings, we define the model directly in hyperbolic space with the Lorentz model. Our model considers each relation in knowledge graphs as a Lorentz rotation from the head entity to the tail entity. We adopt the Lorentzian version distance as the scoring function for measuring the plausibility of triplets. Extensive results on standard knowledge graph completion benchmarks demonstrated that our model achieves competitive results with fewer parameters. In addition, our model get the state-of-the-art performance on datasets of CoDEx-s and CoDEx-m, which are more diverse and challenging than before. Our code is available at https://github.com/llqy123/FHRE.
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