理解与引导基于潜在同构传播的弱监督实体对齐
Understanding and Guiding Weakly Supervised Entity Alignment with Potential Isomorphism Propagation
AI总结：
本文从传播视角分析弱监督实体对齐，证明潜在对齐实体具有同构子图，并提出PipEA框架通过潜在同构传播算子提升各类聚合模型的准确率。
摘要：
弱监督实体对齐（EA）是指仅使用有限数量的种子对齐来识别不同知识图谱（KGs）中等价实体的任务。尽管基于聚合的弱监督实体对齐取得了重大进展，但该设定下的潜在机制仍未被探索。在本文中，我们提出一种传播视角来分析弱监督实体对齐，并解释现有的基于聚合的实体对齐模型。我们的理论分析表明，这些模型本质上是在为成对实体相似性寻找传播算子。我们进一步证明，尽管不同知识图谱存在结构异构性，但基于聚合的实体对齐模型中潜在对齐的实体具有同构子图，这是实体对齐的核心前提但尚未被研究。利用这一洞察，我们引入了潜在同构传播算子来增强跨知识图谱的邻域信息传播。我们开发了一个通用实体对齐框架 PipEA，整合该算子在不改变学习过程的情况下提升各类基于聚合模型的准确率。大量实验证实了我们的理论发现，并展示了 PipEA 相比最先进的弱监督实体对齐方法的显著性能提升。我们的工作不仅推进了该领域，还增强了对基于聚合的弱监督实体对齐的理解。
英文摘要：
Weakly Supervised Entity Alignment (EA) is the task of identifying equivalent entities across diverse knowledge graphs (KGs) using only a limited number of seed alignments. Despite substantial advances in aggregation-based weakly supervised EA, the underlying mechanisms in this setting remain unexplored. In this paper, we present a propagation perspective to analyze weakly supervised EA and explain the existing aggregation-based EA models. Our theoretical analysis reveals that these models essentially seek propagation operators for pairwise entity similarities. We further prove that, despite the structural heterogeneity of different KGs, the potentially aligned entities within aggregation-based EA models have isomorphic subgraphs, which is the core premise of EA but has not been investigated. Leveraging this insight, we introduce a potential isomorphism propagation operator to enhance the propagation of neighborhood information across KGs. We develop a general EA framework, PipEA, incorporating this operator to improve the accuracy of every type of aggregation-based model without altering the learning process. Extensive experiments substantiate our theoretical findings and demonstrate PipEA's significant performance gains over state-of-the-art weakly supervised EA methods. Our work not only advances the field but also enhances our comprehension of aggregation-based weakly supervised EA.
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