提出TeRoR模型，提升时序知识图谱的实体关系建模能力。
TeRoR: Decoupled Temporal Rotation with Relational Circular Region for Temporal Knowledge Graph Embedding
- 分离实体时序演化，对头尾实体独立旋转增强时间建模
- 用圆形区域约束头实体位置，捕捉多种关系映射特性
- 在4个数据集上表现优于现有方法，适合时序知识推理
近年来，随着时序知识图谱（TKGs）的兴起，学习实体与关系表示的研究日益受到关注，涌现出大量TKG嵌入方法。TeRo是一种简单高效的时序知识图谱嵌入方法，但在建模不同关系的映射特性（如一对一、多对一、多对多）方面表现不佳，且在时序信息表达上存在局限。为此，我们提出一种新型TKG嵌入方法TeRoR。该方法将实体嵌入的时序演化解耦，在复数向量空间中对头实体和尾实体分别进行独立旋转，以增强时序信息建模能力。针对关系特性，训练一个半径，将旋转并平移后的头实体约束在以尾实体为中心的圆形区域内，有效捕捉各类关系的映射模式。实验结果表明，TeRoR在四个不同的TKG数据集上达到与当前最优模型相当的性能。
原文摘要 · Abstract (English)
In recent years, with the emergence of Temporal Knowledge Graphs (TKGs), research on learning entity and relation representations in TKGs has attracted increasing attention, giving rise to a large number of TKG embedding methods. TeRo is a simple and efficient temporal knowledge graph embedding approach. However, TeRo does not do well in modeling the mapping properties of various relations, such as one-to-many, many-to-one, and many-to-many. Meanwhile, it also has limitations in the expression of temporal information. To address these issues, we propose a novel TKG embedding method named TeRoR. This method divides the temporal evolution of entity embeddings, and conducts independent rotation transformations on head and tail entities in the complex vector space to strengthen temporal information modeling capacity. In terms of relational characteristics, we train a radius to constrain the rotated and translated head entities within a circular region centered on the tail entity, which effectively captures the diverse mapping properties of relations. Experimental results demonstrate that TeRoR achieves competitive performance against state-of-the-art models on four distinct TKG datasets.
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