  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.
