Temporal knowledge graph completion that predicts missing links for incomplete temporal knowledge graphs (TKG) is gaining increasing attention.Most existing works have achieved good results by incorporating time information into static knowledge graph embedding methods.However, they ignore the contextual nature of the TKG structure, i.e., query-specific subgraph contains both structural and temporal neighboring facts.This paper presents the SToKE, a novel method that employs the pre-trained language model (PLM) to…