@inproceedings{hu-etal-2026-conformal,
title = "Conformal Event Prediction with Temporal Knowledge Graph",
author = "Hu, Cheng and
Cao, Cong and
Yuan, Fangfang and
Guo, Diandian and
Xu, Pin and
Liu, Yu and
Liu, Yanbing",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.258/",
pages = "5233--5248",
ISBN = "979-8-89176-395-1",
abstract = "Event prediction plays a critical role in high-stakes applications such as military operations, public safety, and healthcare. Current methods learn temporal knowledge graphs to predict events at future timestamps, and the predictions directly influence decision-making and resource allocation. However, these methods lack rigorous uncertainty quantification, which limits their reliability for decision-making, especially in high-stakes scenarios where the cost of errors is high. In this paper, we propose CFEP, a conformal prediction framework tailored for event prediction to address this challenge. This is achieved through end-to-end optimization that ensures coverage while improving efficiency. Specifically, we first introduce non-conformity score diffusion, which captures both topological and temporal uncertainty in temporal knowledge graphs. Additionally, we propose an efficiency-aware optimization algorithm to reduce the coverage gap and improve computational efficiency. Experimental results on three public datasets demonstrate that our approach consistently guarantees statistical coverage while improving efficiency. The code and datasets are available at https://github.com/hucheng-IIE/CFEP."
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%0 Conference Proceedings
%T Conformal Event Prediction with Temporal Knowledge Graph
%A Hu, Cheng
%A Cao, Cong
%A Yuan, Fangfang
%A Guo, Diandian
%A Xu, Pin
%A Liu, Yu
%A Liu, Yanbing
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F hu-etal-2026-conformal
%X Event prediction plays a critical role in high-stakes applications such as military operations, public safety, and healthcare. Current methods learn temporal knowledge graphs to predict events at future timestamps, and the predictions directly influence decision-making and resource allocation. However, these methods lack rigorous uncertainty quantification, which limits their reliability for decision-making, especially in high-stakes scenarios where the cost of errors is high. In this paper, we propose CFEP, a conformal prediction framework tailored for event prediction to address this challenge. This is achieved through end-to-end optimization that ensures coverage while improving efficiency. Specifically, we first introduce non-conformity score diffusion, which captures both topological and temporal uncertainty in temporal knowledge graphs. Additionally, we propose an efficiency-aware optimization algorithm to reduce the coverage gap and improve computational efficiency. Experimental results on three public datasets demonstrate that our approach consistently guarantees statistical coverage while improving efficiency. The code and datasets are available at https://github.com/hucheng-IIE/CFEP.
%U https://aclanthology.org/2026.findings-acl.258/
%P 5233-5248
Markdown (Informal)
[Conformal Event Prediction with Temporal Knowledge Graph](https://aclanthology.org/2026.findings-acl.258/) (Hu et al., Findings 2026)
ACL
- Cheng Hu, Cong Cao, Fangfang Yuan, Diandian Guo, Pin Xu, Yu Liu, and Yanbing Liu. 2026. Conformal Event Prediction with Temporal Knowledge Graph. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5233–5248, San Diego, California, United States. Association for Computational Linguistics.