A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs

Yimin Deng; Yuxia Wu; Yejing Wang; Guoshuai Zhao; Li Zhu; Qidong Liu; Derong Xu; Zichuan Fu; Xian Wu; Yefeng Zheng; Xiangyu Zhao; Xueming Qian

doi:10.18653/v1/2025.findings-acl.1056

A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs

Yimin Deng
, Yuxia Wu
, Yejing Wang
, Guoshuai Zhao^*
, Li Zhu^*
, Qidong Liu
, Derong Xu
, Zichuan Fu
, Xian Wu^*
, Yefeng Zheng
, Xiangyu Zhao^*
, Xueming Qian

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 Multi-Expert Structural-Semantic Hybrid (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.

Original language	English
Title of host publication	Findings of the Association for Computational Linguistics
Subtitle of host publication	ACL 2025
Editors	Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Publisher	Association for Computational Linguistics (ACL)
Pages	20553-20565
Number of pages	13
ISBN (Electronic)	9798891762565
DOIs	https://doi.org/10.18653/v1/2025.findings-acl.1056
State	Published - 2025
Externally published	Yes
Event	63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025 - Vienna, Austria Duration: 27 Jul 2025 → 1 Aug 2025

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)	0736-587X

Conference

Conference	63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025
Country/Territory	Austria
City	Vienna
Period	27/07/25 → 1/08/25

Access to Document

10.18653/v1/2025.findings-acl.1056

Cite this

Deng, Y., Wu, Y., Wang, Y., Zhao, G., Zhu, L., Liu, Q., Xu, D., Fu, Z., Wu, X., Zheng, Y., Zhao, X., & Qian, X. (2025). A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs. In W. Che, J. Nabende, E. Shutova, & M. T. Pilehvar (Eds.), Findings of the Association for Computational Linguistics: ACL 2025 (pp. 20553-20565). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2025.findings-acl.1056

Deng, Yimin ; Wu, Yuxia ; Wang, Yejing et al. / A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs. Findings of the Association for Computational Linguistics: ACL 2025. editor / Wanxiang Che ; Joyce Nabende ; Ekaterina Shutova ; Mohammad Taher Pilehvar. Association for Computational Linguistics (ACL), 2025. pp. 20553-20565 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

@inproceedings{b678c8c914484fd996d6868e7ad7debb,

title = "A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs",

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 Multi-Expert Structural-Semantic Hybrid (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.",

author = "Yimin Deng and Yuxia Wu and Yejing Wang and Guoshuai Zhao and Li Zhu and Qidong Liu and Derong Xu and Zichuan Fu and Xian Wu and Yefeng Zheng and Xiangyu Zhao and Xueming Qian",

note = "Publisher Copyright: {\textcopyright} 2025 Association for Computational Linguistics.; 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025 ; Conference date: 27-07-2025 Through 01-08-2025",

year = "2025",

doi = "10.18653/v1/2025.findings-acl.1056",

language = "英语",

series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",

publisher = "Association for Computational Linguistics (ACL)",

pages = "20553--20565",

editor = "Wanxiang Che and Joyce Nabende and Ekaterina Shutova and Pilehvar, \{Mohammad Taher\}",

booktitle = "Findings of the Association for Computational Linguistics",

address = "澳大利亚",

}

Deng, Y, Wu, Y, Wang, Y, Zhao, G, Zhu, L, Liu, Q, Xu, D, Fu, Z, Wu, X, Zheng, Y, Zhao, X & Qian, X 2025, A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs. in W Che, J Nabende, E Shutova & MT Pilehvar (eds), Findings of the Association for Computational Linguistics: ACL 2025. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), pp. 20553-20565, 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, 27/07/25. https://doi.org/10.18653/v1/2025.findings-acl.1056

A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs. / Deng, Yimin; Wu, Yuxia; Wang, Yejing et al.
Findings of the Association for Computational Linguistics: ACL 2025. ed. / Wanxiang Che; Joyce Nabende; Ekaterina Shutova; Mohammad Taher Pilehvar. Association for Computational Linguistics (ACL), 2025. p. 20553-20565 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs

AU - Deng, Yimin

AU - Wu, Yuxia

AU - Wang, Yejing

AU - Zhao, Guoshuai

AU - Zhu, Li

AU - Liu, Qidong

AU - Xu, Derong

AU - Fu, Zichuan

AU - Wu, Xian

AU - Zheng, Yefeng

AU - Zhao, Xiangyu

AU - Qian, Xueming

PY - 2025

Y1 - 2025

N2 - 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 Multi-Expert Structural-Semantic Hybrid (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.

AB - 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 Multi-Expert Structural-Semantic Hybrid (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.

UR - https://www.scopus.com/pages/publications/105028559011

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DO - 10.18653/v1/2025.findings-acl.1056

M3 - 会议稿件

AN - SCOPUS:105028559011

T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics

SP - 20553

EP - 20565

BT - Findings of the Association for Computational Linguistics

A2 - Che, Wanxiang

A2 - Nabende, Joyce

A2 - Shutova, Ekaterina

A2 - Pilehvar, Mohammad Taher

PB - Association for Computational Linguistics (ACL)

T2 - 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025

Y2 - 27 July 2025 through 1 August 2025

ER -

Deng Y, Wu Y, Wang Y, Zhao G, Zhu L, Liu Q et al. A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs. In Che W, Nabende J, Shutova E, Pilehvar MT, editors, Findings of the Association for Computational Linguistics: ACL 2025. Association for Computational Linguistics (ACL). 2025. p. 20553-20565. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2025.findings-acl.1056

A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs

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