发表机构
School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对现有实例级因果对泛化缺陷，提出抽象事件因果规则（AECR）及相关归纳系统，构建知识库并设计 AR-GCAE 模型注入 AECR，在 CGEP 任务中提升了事件因果推理与预测性能，尤其对稀有未见样本效果显著。
AI 中文摘要
以事件为中心的智能分析系统高度依赖显式因果事件知识，用于风险预警、决策支持和叙事理解。然而，现有实例级因果对在低频长尾事件及未见事件组合上存在严重泛化缺陷。为解决该问题，本研究提出抽象事件因果规则（Abstract Event Causal Rule, AECR），这是一种新颖的关系级因果抽象范式，能将具体因果对转化为保留内在因果关系的广义抽象因果逻辑。我们设计了多智能体 concrete-to-abstract 因果归纳（Concrete-to-Abstract Causal Induction, CACI）系统，结合相似度约束聚类，从含噪原始因果数据中提炼可信的 AECR，基于此构建了两个完整的 AECR 知识库。为验证抽象因果知识的实用价值，我们提出抽象规则引导因果注意力编码器（Abstract Rule-Guided Causal Attention Encoder, AR-GCAE），通过规则引导注意力层和门控表示融合，将检索到的 AECR 注入因果图事件预测（causality Graph Event Prediction, CGEP）基准任务。定量实验结果显示，应用 AECR 可显著增强事件因果推理的泛化能力，为事件预测带来持续性能提升，在稀有和未见事件样本上的增益最为显著。
英文摘要
Event-centric intelligent analytical systems heavily depend on explicit causal event knowledge for risk early warning, decision-making support and narrative comprehension. Nevertheless, existing instance-level causal pairs suffer severe generalization deficits on low-frequency long-tail and unseen event combinations. To address this limitation, this work proposes Abstract Event Causal Rule (AECR), a novel relation-level causal abstraction paradigm that transforms concrete cause-effect pairs into generalized abstract causal logic while retaining their intrinsic causal relationships. We design a multi-agent Concrete-to-Abstract Causal Induction (CACI) system coupled with similarity-constrained clustering to distill trustworthy AECRs from noisy raw causal data, based on which two complete AECR knowledge bases are built. To validate the practical utility of abstract causal knowledge, we propose an Abstract Rule-Guided Causal Attention Encoder (AR-GCAE), which injects the retrieved AECRs into the causality Graph Event Prediction (CGEP) benchmark task via rule-guided attention layers and gated representation fusion. Quantitative experimental results reveal that applying AECRs substantially strengthens the generalization capacity of event causal reasoning and brings consistent performance improvements to event prediction, with the most prominent gains observed on rare and unseen event samples.
