Modeling evolving knowledge over temporal knowledge graphs (TKGs) has become a heated topic. Various methods have been proposed to forecast links on TKGs. Most of them are embedding-based, where hidden representations are learned to represent knowledge graph (KG) entities and relations based on the observed graph contexts. Although these methods show strong performance on traditional TKG forecasting (TKGF) benchmarks, they face a strong challenge in modeling the unseen zero-shot relations that have no prior graph context. In this paper, we try to mitigate this problem as follows. We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods. LLM-empowered representations can capture the semantic information in the relation descriptions. This makes the relations, whether seen or unseen, with similar semantic meanings stay close in the embedding space, enabling TKGF models to recognize zero-shot relations even without any observed graph context. Experimental results show that our approach helps TKGF models to achieve much better performance in forecasting the facts with previously unseen relations, while still maintaining their ability in link forecasting regarding seen relations.

翻译：时序知识图谱（TKG）中的演化知识建模已成为热点课题。现有方法多聚焦于时序知识图谱上的链接预测，其中绝大多数为基于嵌入的方法，通过学习隐藏表征来表征知识图谱实体与关系在观测图上下文中的特征。尽管这些方法在传统TKG预测基准上表现优异，但在建模无先验图上下文的未见零样本关系时面临严峻挑战。本文尝试通过以下方式缓解该问题：首先将KG关系的文本描述输入大语言模型以生成关系表征，随后将其引入基于嵌入的TKG预测方法。大语言模型赋能的表征能够捕捉关系描述中的语义信息，使得语义相似的已见与未见关系在嵌入空间中保持邻近，从而令TKG预测模型无需任何观测图上下文即可识别零样本关系。实验结果表明，本方法在显著提升TKG预测模型对含未见关系事实预测性能的同时，仍能保持其对已见关系链接预测的能力。