通过迭代增强种子节点，提升异构知识图谱实体对齐效果
SEG:Seeds-Enhanced Iterative Refinement Graph Neural Network for Entity Alignment
- 用种子节点引导，动态优化软标签以融合多源邻居特征
- 在多个数据集上超越现有半监督方法，显著提升对齐准确率
- 适合处理大规模、稀疏连接的跨图实体匹配任务
实体对齐对于合并不同知识图谱中的信息至关重要，旨在匹配语义相同的实体。传统方法基于嵌入相似性进行半监督学习，但因数据源差异导致对齐实体的邻域结构非同构，尤其影响少见和稀疏连接实体的对齐。本文提出一种软标签传播框架，整合多源数据并实现迭代种子增强，有效应对大规模数据下的可扩展性挑战。该框架利用种子锚定，选取最优关系对生成富含邻域特征与语义关系信息的软标签。采用双向加权联合损失函数，缩小正样本距离，差异化处理负样本，充分考虑非同构邻域结构。实验表明，该方法在多个数据集上优于现有半监督方法，显著提升实体对齐质量。
原文摘要 · Abstract (English)
Entity alignment is crucial for merging knowledge across knowledge graphs, as it matches entities with identical semantics. The standard method matches these entities based on their embedding similarities using semi-supervised learning. However, diverse data sources lead to non-isomorphic neighborhood structures for aligned entities, complicating alignment, especially for less common and sparsely connected entities. This paper presents a soft label propagation framework that integrates multi-source data and iterative seed enhancement, addressing scalability challenges in handling extensive datasets where scale computing excels. The framework uses seeds for anchoring and selects optimal relationship pairs to create soft labels rich in neighborhood features and semantic relationship data. A bidirectional weighted joint loss function is implemented, which reduces the distance between positive samples and differentially processes negative samples, taking into account the non-isomorphic neighborhood structures. Our method outperforms existing semi-supervised approaches, as evidenced by superior results on multiple datasets, significantly improving the quality of entity alignment.
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