重新审视连续知识图谱嵌入中的灾难性遗忘
Revisiting Catastrophic Forgetting in Continual Knowledge Graph Embedding
AI总结：
本文提出修正的连续知识图谱嵌入评估协议，揭示实体干扰对模型性能的影响，发现忽略该效应可能导致性能高估达25%。
摘要：
知识图谱嵌入（KGEs）支持广泛的知识图谱（KGs）下游任务。在实践中，KGs随着新实体和事实的添加而演变，推动了连续知识图谱嵌入（CKGE）方法，以随时间更新嵌入。当前CKGE方法主要通过限制现有嵌入的变化来应对灾难性遗忘（即先前学习任务性能下降）。然而，我们发现这种观点不完整。当引入新实体时，其嵌入可能干扰先前学习的嵌入，导致模型预测这些新实体而非先前正确的答案。这种现象称为实体干扰，长期以来被忽视，且未在当前CKGE评估协议中考虑。因此，灾难性遗忘的评估变得误导，CKGE方法的性能被系统性高估。为解决此问题，我们引入了考虑实体干扰的修正CKGE评估协议。通过在多个基准上的实验，我们表明忽略此效应可能导致性能高估达25%，特别是在实体增长显著的场景中。我们进一步分析了不同CKGE方法和KGE模型如何受不同遗忘来源的影响，并引入了针对CKGE的灾难性遗忘度量指标。
英文摘要：
Knowledge Graph Embeddings (KGEs) support a wide range of downstream tasks over Knowledge Graphs (KGs). In practice, KGs evolve as new entities and facts are added, motivating Continual Knowledge Graph Embedding (CKGE) methods that update embeddings over time. Current CKGE approaches address catastrophic forgetting (i.e., the performance degradation on previously learned tasks) primarily by limiting changes to existing embeddings. However, we show that this view is incomplete. When new entities are introduced, their embeddings can interfere with previously learned ones, causing the model to predict them in place of previously correct answers. This phenomenon, which we call entity interference, has been largely overlooked and is not accounted for in current CKGE evaluation protocols. As a result, the assessment of catastrophic forgetting becomes misleading, and CKGE methods performance is systematically overestimated. To address this issue, we introduce a corrected CKGE evaluation protocol that accounts for entity interference. Through experiments on multiple benchmarks, we show that ignoring this effect can lead to performance overestimation of up to 25%, particularly in scenarios with significant entity growth. We further analyze how different CKGE methods and KGE models are affected by the different sources of forgetting, and introduce a catastrophic forgetting metric tailored to CKGE.
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