In recent years, 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
naturally face a strong challenge when they are asked to model the unseen
zero-shot relations that has 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.

通过将知识图谱关系的文本描述输入到大型语言模型中，我们提出了一种新的方法以解决传统知识图谱预测中的无先验图谱上下文的零样本关系建模问题，并通过引入大型语言模型增强的关系表示，使传统模型能够识别没有观察到的关系，在预测未知关系方面取得更好的性能。