融合结构与语义的多专家框架，提升时序知识图谱预测能力
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
- 设计三类专家模块，协同处理结构与语义信息
- 在三个数据集上显著优于现有方法，提升预测准确性
- 适合需要跨时序场景推理的研究者与开发者
时序知识图谱推理旨在基于已有事实预测未来事件，在多个下游任务中具有关键作用。以往方法仅关注图结构学习或语义推理，未能融合双重推理视角以应对不同预测场景。同时，它们无法捕捉历史事件与非历史事件之间的本质差异，限制了在不同时间上下文中的泛化能力。为此，我们提出多专家结构-语义混合框架（MESH），采用三种专家模块整合结构与语义信息，引导不同事件的推理过程。在三个数据集上的大量实验表明该方法有效。
原文摘要 · Abstract (English)
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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