@inproceedings{jiang-etal-2026-dyhist,
title = "{D}y{H}ist: Query-Aware Historical Dynamics Modeling for Temporal Knowledge Graph Extrapolation",
author = "Jiang, Peiyuan and
Liu, Yao and
Wen, Yubo and
Gan, Yanglei and
Liu, Lu and
Zhang, Ruojie and
Yang, Jiaye and
Xue, Yuewei and
Deng, Yajiao and
Guo, Xiaoyu and
Liu, Qiao",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.361/",
pages = "7557--7574",
ISBN = "979-8-89176-462-0",
abstract = "Temporal Knowledge Graph (TKG) extrapolation aims to predict future facts from observed historical events. Existing methods usually achieve this goal by jointly modeling local historical evolution and long-range historical contexts. First, local evolution modeling often summarizes local history into an overall evolving representation, where query-relevant local deviations may not be sufficiently exploited as useful predictive features. Second, existing long-range history modeling often treats historical recurrence as a stable cue, while overlooking that its reliability may vary across relations and recent contexts. To address these issues, we propose \textbf{DyHist}, a query-aware historical dynamics modeling framework for TKG extrapolation. DyHist captures local dynamics by separating recent evidence from medium-range context and explicitly modeling their query-relevant deviations as predictive features. It further uses a relation-aware historical prior to calibrate candidate scores with time-decayed support, recent trends, and relation-specific strength, enabling recurrence signals to contribute differently across relations and recent contexts. Experiments on four benchmarks show that DyHist achieves the best MRR against recent baselines. Code is available at https://github.com/PANPANKK/DyHist."
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<abstract>Temporal Knowledge Graph (TKG) extrapolation aims to predict future facts from observed historical events. Existing methods usually achieve this goal by jointly modeling local historical evolution and long-range historical contexts. First, local evolution modeling often summarizes local history into an overall evolving representation, where query-relevant local deviations may not be sufficiently exploited as useful predictive features. Second, existing long-range history modeling often treats historical recurrence as a stable cue, while overlooking that its reliability may vary across relations and recent contexts. To address these issues, we propose DyHist, a query-aware historical dynamics modeling framework for TKG extrapolation. DyHist captures local dynamics by separating recent evidence from medium-range context and explicitly modeling their query-relevant deviations as predictive features. It further uses a relation-aware historical prior to calibrate candidate scores with time-decayed support, recent trends, and relation-specific strength, enabling recurrence signals to contribute differently across relations and recent contexts. Experiments on four benchmarks show that DyHist achieves the best MRR against recent baselines. Code is available at https://github.com/PANPANKK/DyHist.</abstract>
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%0 Conference Proceedings
%T DyHist: Query-Aware Historical Dynamics Modeling for Temporal Knowledge Graph Extrapolation
%A Jiang, Peiyuan
%A Liu, Yao
%A Wen, Yubo
%A Gan, Yanglei
%A Liu, Lu
%A Zhang, Ruojie
%A Yang, Jiaye
%A Xue, Yuewei
%A Deng, Yajiao
%A Guo, Xiaoyu
%A Liu, Qiao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F jiang-etal-2026-dyhist
%X Temporal Knowledge Graph (TKG) extrapolation aims to predict future facts from observed historical events. Existing methods usually achieve this goal by jointly modeling local historical evolution and long-range historical contexts. First, local evolution modeling often summarizes local history into an overall evolving representation, where query-relevant local deviations may not be sufficiently exploited as useful predictive features. Second, existing long-range history modeling often treats historical recurrence as a stable cue, while overlooking that its reliability may vary across relations and recent contexts. To address these issues, we propose DyHist, a query-aware historical dynamics modeling framework for TKG extrapolation. DyHist captures local dynamics by separating recent evidence from medium-range context and explicitly modeling their query-relevant deviations as predictive features. It further uses a relation-aware historical prior to calibrate candidate scores with time-decayed support, recent trends, and relation-specific strength, enabling recurrence signals to contribute differently across relations and recent contexts. Experiments on four benchmarks show that DyHist achieves the best MRR against recent baselines. Code is available at https://github.com/PANPANKK/DyHist.
%U https://aclanthology.org/2026.findings-emnlp.361/
%P 7557-7574
Markdown (Informal)
[DyHist: Query-Aware Historical Dynamics Modeling for Temporal Knowledge Graph Extrapolation](https://aclanthology.org/2026.findings-emnlp.361/) (Jiang et al., Findings 2026)
ACL
- Peiyuan Jiang, Yao Liu, Yubo Wen, Yanglei Gan, Lu Liu, Ruojie Zhang, Jiaye Yang, Yuewei Xue, Yajiao Deng, Xiaoyu Guo, and Qiao Liu. 2026. DyHist: Query-Aware Historical Dynamics Modeling for Temporal Knowledge Graph Extrapolation. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7557–7574, Budapest, Hungary. Association for Computational Linguistics.