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

• URL: http://arxiv.org/abs/2506.14235v1
• Date: Tue, 17 Jun 2025 06:49:13 GMT
• Title: A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
• Authors: Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian,
• Abstract summary: Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks.<n>Previous methods focused on either graph structure learning or semantic reasoning, failing to integrate dual reasoning perspectives.<n>We propose a Multi-Expert Structural-Semantic Hybrid framework that employs three kinds of expert modules to integrate both structural and semantic information.
• Score: 66.98208997876783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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