Temporal Knowledge Graph (TKG) reasoning aims to infer future events from historical facts. Recent advances in large language models (LLMs) have shown that in-context learning can effectively enhance temporal reasoning. While existing approaches over-rely on historical information and overlook crucial non-historical factors, which CALENDAR addresses. However, CALENDAR overlooks event recency and relies heavily on global principles, which leads to inaccuracies in TKG reasoning. To address this limitation, we propose CALENDAR+ (i.e., in-context ContrAstive Learning tEmporal kNowleDge grAph Reasoning), a novel approach that integrates contrastive demonstrations to improve in-context reasoning. In CALENDAR+, we propose a demonstration candidate generation with high-order information method, which generates demonstration candidates from both historical and non-historical information. Moreover, we devise a time-aware contrastive importance based demonstration selection method to emphasize the most informative examples across time. Furthermore, we design a global–local chain-of-history based demonstration format which provides explicit negative principles that guide the model to avoid over-reliance on global and local histories. Extensive experiments show that CALENDAR+ achieves consistent improvements of over 1% across multiple TKG datasets, including Hits@10 of 60.10% on ICEWS14, 53.30% on ICEWS18, and 69.95% on ICEWS05-15, with an MRR gain of 5.67% over the strongest baseline.
Li et al. (Thu,) studied this question.