历史重演：克服事件中心时序知识图谱补全中的灾难性遗忘
History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion
AI总结：
针对事件中心时序知识图谱补全中的灾难性遗忘问题，本文提出一种通用持续训练框架，结合时序正则化与聚类经验回放，在适应新事件的同时有效保留旧知识。
摘要：
时序知识图谱（TKG）补全模型通常依赖于在训练期间能够访问整个图谱。然而，在现实场景中，TKG数据往往随着事件的发生而增量式接收，导致数据分布随时间呈现动态非平稳特性。虽然可以通过对现有方法进行微调来使其适应不断演化的TKG数据，但这可能导致遗忘先前学到的模式。另一种做法是利用更新后的完整TKG重新训练模型，这可以缓解遗忘问题，但计算负担沉重。为应对这些挑战，我们提出了一个通用的持续训练框架，该框架适用于任何TKG补全方法，并利用了两个关键思想：（i）一种时序正则化方法，鼓励重新利用较不重要的模型参数来学习新知识，以及（ii）一种基于聚类的经验回放方法，通过有选择地仅保留过去数据的一小部分来强化过去的知识。我们在广泛使用的事件中心TKG数据集上的实验结果表明，所提出的持续训练框架在适应新事件的同时能够有效减少灾难性遗忘。此外，我们进行了消融研究，以展示所提出框架中每个组成部分的有效性。最后，我们研究了用于经验回放的内存与基于聚类的采样策略所带来的收益之间的关系。
英文摘要：
Temporal knowledge graph (TKG) completion models typically rely on having access to the entire graph during training. However, in real-world scenarios, TKG data is often received incrementally as events unfold, leading to a dynamic non-stationary data distribution over time. While one could incorporate fine-tuning to existing methods to allow them to adapt to evolving TKG data, this can lead to forgetting previously learned patterns. Alternatively, retraining the model with the entire updated TKG can mitigate forgetting but is computationally burdensome. To address these challenges, we propose a general continual training framework that is applicable to any TKG completion method, and leverages two key ideas: (i) a temporal regularization that encourages repurposing of less important model parameters for learning new knowledge, and (ii) a clustering-based experience replay that reinforces the past knowledge by selectively preserving only a small portion of the past data. Our experimental results on widely used event-centric TKG datasets demonstrate the effectiveness of our proposed continual training framework in adapting to new events while reducing catastrophic forgetting. Further, we perform ablation studies to show the effectiveness of each component of our proposed framework. Finally, we investigate the relation between the memory dedicated to experience replay and the benefit gained from our clustering-based sampling strategy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
