CALENDAR+: in-context contrastive learning for temporal knowledge graph reasoning
Year: 2026
Type: paratext
Abstract: 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 C ontr A stive L earning t E mporal k N owle D ge gr A ph R easoning), 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.
Source: Complex & Intelligent Systems
Authors: Xingyi Li, Jiapeng Wang, Boyuan Jia, Xiang Cheng, Sen Su
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Award: G7215948923
OpenAlex ID: W7131636262