@inproceedings{deng-etal-2025-multi,
title = "A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs",
author = "Deng, Yimin and
Wu, Yuxia and
Wang, Yejing and
Zhao, Guoshuai and
Zhu, Li and
Liu, Qidong and
Xu, Derong and
Fu, Zichuan and
Wu, Xian and
Zheng, Yefeng and
Zhao, Xiangyu and
Qian, Xueming",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1056/",
doi = "10.18653/v1/2025.findings-acl.1056",
pages = "20553--20565",
ISBN = "979-8-89176-256-5",
abstract = "Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on either graph structure learning or semantic reasoning, failing to integrate dual reasoning perspectives to handle different prediction scenarios. Moreover, they lack the capability to capture the inherent differences between historical and non-historical events, which limits their generalization across different temporal contexts. To this end, we propose a **M**ulti-**E**xpert **S**tructural-**S**emantic **H**ybrid (MESH) framework that employs three kinds of expert modules to integrate both structural and semantic information, guiding the reasoning process for different events. Extensive experiments on three datasets demonstrate the effectiveness of our approach."
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<abstract>Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on either graph structure learning or semantic reasoning, failing to integrate dual reasoning perspectives to handle different prediction scenarios. Moreover, they lack the capability to capture the inherent differences between historical and non-historical events, which limits their generalization across different temporal contexts. To this end, we propose a **M**ulti-**E**xpert **S**tructural-**S**emantic **H**ybrid (MESH) framework that employs three kinds of expert modules to integrate both structural and semantic information, guiding the reasoning process for different events. Extensive experiments on three datasets demonstrate the effectiveness of our approach.</abstract>
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%0 Conference Proceedings
%T A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
%A Deng, Yimin
%A Wu, Yuxia
%A Wang, Yejing
%A Zhao, Guoshuai
%A Zhu, Li
%A Liu, Qidong
%A Xu, Derong
%A Fu, Zichuan
%A Wu, Xian
%A Zheng, Yefeng
%A Zhao, Xiangyu
%A Qian, Xueming
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F deng-etal-2025-multi
%X Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on either graph structure learning or semantic reasoning, failing to integrate dual reasoning perspectives to handle different prediction scenarios. Moreover, they lack the capability to capture the inherent differences between historical and non-historical events, which limits their generalization across different temporal contexts. To this end, we propose a **M**ulti-**E**xpert **S**tructural-**S**emantic **H**ybrid (MESH) framework that employs three kinds of expert modules to integrate both structural and semantic information, guiding the reasoning process for different events. Extensive experiments on three datasets demonstrate the effectiveness of our approach.
%R 10.18653/v1/2025.findings-acl.1056
%U https://aclanthology.org/2025.findings-acl.1056/
%U https://doi.org/10.18653/v1/2025.findings-acl.1056
%P 20553-20565
Markdown (Informal)
[A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs](https://aclanthology.org/2025.findings-acl.1056/) (Deng et al., Findings 2025)
ACL
- Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, and Xueming Qian. 2025. A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs. In Findings of the Association for Computational Linguistics: ACL 2025, pages 20553–20565, Vienna, Austria. Association for Computational Linguistics.