发表机构
University of New South Wales; The University of Sydney（新南威尔士大学; 悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对持续知识图谱嵌入中实体加入导致的候选集干扰问题，提出MEOR正则化方法，在多个基准数据集上提升了历史候选集的排名性能，验证了该方法的有效性。
AI 中文摘要
持续知识图谱嵌入会随着图谱的增长更新实体和关系的表示。现有方法主要解决灾难性遗忘问题，但实体的加入也会改变每个兼容查询的候选集合。因此，即使历史答案的分数及其在旧实体中的排序保持不变，其排名仍可能下降。我们将这种效应形式化为候选集干扰，并提出匹配超额排序器正则化（MEOR），这是一种主机级目标函数，用于比较平滑的答案相关新实体压力与无分数、结构匹配的旧参考。其单侧惩罚仅在新实体竞争超过匹配参考时生效，从而保留主机学习器对合法新实体的信号。在ENTITY-ComplEx上的8次配对运行中，MEOR相比重放方法将历史当前候选集的平均倒数排名（MRR）提高了0.0057，将候选集干扰降低了0.0055，对应的单侧95%下界分别为0.0052和0.0051。它满足旧实体集排名保留和新实体获取的标准，且相比持久校准、匹配最大正则化器（MMR）和不匹配旧正则化器（UOR），能提高历史当前候选集的MRR。直接消融实验验证了其参考构建和聚合各组件的有效性。在所有10个报告的FBInc-S和FBInc-L的主机及骨干设置中，添加MEOR均能改善历史排名，且每个配对的95%置信区间均不包含零。这些结果表明，实体加入是持续排名损失的一个独立来源，且可在不替换底层嵌入架构或持续学习器的情况下对其进行控制。
英文摘要
Continual knowledge graph embedding updates entity and relation representations as a graph grows. Existing methods primarily address catastrophic forgetting, but entity admission also changes the candidate universe of every compatible query. A historical answer can therefore lose rank even when its score and its ordering among old entities are preserved. We formalize this effect as candidate-set interference and introduce Matched Excess-Outranker Regularization (MEOR), a host-level objective that compares smooth answer-relative newcomer pressure with score-blind, structurally matched old references. Its one-sided penalty acts only when newcomer competition exceeds the matched reference, preserving the host learner's signal for legitimate new entities. Across eight paired runs on ENTITY-ComplEx, MEOR improves historical current-universe mean reciprocal rank (MRR) by 0.0057 over replay and reduces candidate-set interference by 0.0055, with one-sided 95% lower bounds of 0.0052 and 0.0051, respectively. It satisfies the preservation criteria for old-universe ranking and newcomer acquisition and improves historical current-universe MRR over persistent calibration, matched maximum regularizer (MMR), and unmatched old regularizer (UOR). Direct ablations support each component of its reference construction and aggregation. Adding MEOR also improves historical ranking in all ten reported FBInc-S and FBInc-L host and backbone settings, with every paired 95% confidence interval excluding zero. These results establish candidate admission as a distinct source of continual rank loss and show that it can be controlled without replacing the underlying embedding architecture or continual learner.
CommentsThis version removes placeholder ACM publication metadata. Currently under review. 10 pages, 2 figures
