通过迭代优化提升实体对齐精度，解决异构知识图谱中种子对质量差的问题。
SE-GNN: Seed Expanded-Aware Graph Neural Network with Iterative Optimization for Semi-supervised Entity Alignment
- 融合语义与结构特征，用条件过滤生成高质量初始种子对。
- 设计局部全局感知机制，增强嵌入表示以应对图谱结构差异。
- 采用相似度阈值与双向最近邻结合的修正策略，降低噪声影响。
实体对齐旨在利用预对齐的种子对在不同知识图谱（KGs）中发现其他等价实体，广泛应用于图融合领域。然而，随着知识图谱规模扩大，人工标注种子对变得困难。现有方法依赖单一结构信息聚合获取实体嵌入以识别潜在种子对，减少对预对齐种子的依赖。但由于知识图谱存在结构异质性，仅使用单一结构信息得到的潜在种子对质量不佳。此外，尽管已有研究通过半监督迭代改进种子对质量，却低估了噪声种子对带来的嵌入失真对对齐效果的影响。为此，本文提出一种带有迭代优化的种子扩展感知图神经网络（SE-GNN）。首先，利用实体的语义属性与结构特征，并结合条件过滤机制，生成高质量初始潜在种子对。其次，设计局部与全局感知机制，引入初始潜在种子对并融合局部与全局信息，获得更全面的实体嵌入表示，缓解知识图谱结构异质性的影响，为种子对优化奠定基础。最后，设计相似度阈值与双向最近邻结合的嵌入修正策略，作为筛选迭代潜在种子对的过滤机制，并通过嵌入修正消除嵌入失真。
原文摘要 · Abstract (English)
Entity alignment aims to use pre-aligned seed pairs to find other equivalent entities from different knowledge graphs (KGs) and is widely used in graph fusion-related fields. However, as the scale of KGs increases, manually annotating pre-aligned seed pairs becomes difficult. Existing research utilizes entity embeddings obtained by aggregating single structural information to identify potential seed pairs, thus reducing the reliance on pre-aligned seed pairs. However, due to the structural heterogeneity of KGs, the quality of potential seed pairs obtained using only a single structural information is not ideal. In addition, although existing research improves the quality of potential seed pairs through semi-supervised iteration, they underestimate the impact of embedding distortion produced by noisy seed pairs on the alignment effect. In order to solve the above problems, we propose a seed expanded-aware graph neural network with iterative optimization for semi-supervised entity alignment, named SE-GNN. First, we utilize the semantic attributes and structural features of entities, combined with a conditional filtering mechanism, to obtain high-quality initial potential seed pairs. Next, we designed a local and global awareness mechanism. It introduces initial potential seed pairs and combines local and global information to obtain a more comprehensive entity embedding representation, which alleviates the impact of KGs structural heterogeneity and lays the foundation for the optimization of initial potential seed pairs. Then, we designed the threshold nearest neighbor embedding correction strategy. It combines the similarity threshold and the bidirectional nearest neighbor method as a filtering mechanism to select iterative potential seed pairs and also uses an embedding correction strategy to eliminate the embedding distortion.
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