In the realm of event prediction, temporal knowledge graph forecasting (TKGF) stands as a pivotal technique. Previous approaches face the challenges of not utilizing experience during testing and relying on a single short-term history, which limits adaptation to evolving data. In this paper, we introduce the Online Neural-Symbolic Event Prediction (ONSEP) framework, which innovates by integrating dynamic causal rule mining (DCRM) and dual history augmented generation (DHAG). DCRM dynamically constructs causal rules from real-time data, allowing for swift adaptation to new causal relationships. In parallel, DHAG merges short-term and long-term historical contexts, leveraging a bi-branch approach to enrich event prediction. Our framework demonstrates notable performance enhancements across diverse datasets, with significant Hit@k (k=1,3,10) improvements, showcasing its ability to augment large language models (LLMs) for event prediction without necessitating extensive retraining. The ONSEP framework not only advances the field of TKGF but also underscores the potential of neural-symbolic approaches in adapting to dynamic data environments.

翻译：在事件预测领域，时序知识图谱预测是一项关键技术。现有方法面临在测试阶段无法利用经验以及依赖单一短期历史记录的挑战，这限制了对演化数据的适应能力。本文提出在线神经符号事件预测框架，其创新性在于整合了动态因果规则挖掘与双重历史增强生成技术。动态因果规则挖掘能够从实时数据中动态构建因果规则，从而快速适应新的因果关系。同时，双重历史增强生成融合短期与长期历史上下文，采用双分支方法以丰富事件预测。我们的框架在多个数据集上均表现出显著的性能提升，在Hit@k指标上取得明显进步，这展示了其在不需大量重新训练的情况下增强大语言模型事件预测能力的效果。该框架不仅推动了时序知识图谱预测领域的发展，也凸显了神经符号方法在适应动态数据环境方面的潜力。