ClusterEA：基于随机训练和归一化小批量相似度的可扩展实体对齐
ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities
摘要：
实体对齐的目的是在不同的知识图谱中找到等价的实体。基于嵌入的方法近年来主导了EA任务。这些方法面临的问题来自嵌入向量的几何属性，包括hubness和隔离。为了解决这些几何问题，许多归一化方法已被采用的EA。然而，越来越大的规模的KG渲染EA模型很难采用规范化过程，从而限制了它们在现实世界中的应用。为了应对这一挑战，我们提出了ClusterEA，这是一个通用框架，能够通过利用具有高实体等效率的小批量标准化方法来扩展EA模型并增强其结果。QuantiterEA包含三个组件来对齐大规模KG之间的实体，包括随机训练，QuantiterSampler和SparseFusion。它首先以随机的方式为EA训练一个大规模的Siamese GNN，以产生实体嵌入。在此基础上，提出了一种新的用于高度重叠小批量采样的随机采样器策略。最后，QuarterEA结合了SparseFusion，它对局部和全局相似性进行归一化，然后融合所有相似性矩阵以获得最终的相似性矩阵。在EA基准测试上使用真实数据集进行的广泛实验提供了对所提出的框架的深入了解，并表明它能够在Hits@1方面超过最先进的可扩展EA框架多达8倍。
英文摘要：
Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods face problems that come from the geometric properties of embedding vectors, including hubness and isolation. To solve these geometric problems, many normalization approaches have been adopted for EA. However, the increasing scale of KGs renders it hard for EA models to adopt the normalization processes, thus limiting their usage in real-world applications. To tackle this challenge, we present ClusterEA, a general framework that is capable of scaling up EA models and enhancing their results by leveraging normalization methods on mini-batches with a high entity equivalent rate. ClusterEA contains three components to align entities between large-scale KGs, including stochastic training, ClusterSampler, and SparseFusion. It first trains a large-scale Siamese GNN for EA in a stochastic fashion to produce entity embeddings. Based on the embeddings, a novel ClusterSampler strategy is proposed for sampling highly overlapped mini-batches. Finally, ClusterEA incorporates SparseFusion, which normalizes local and global similarity and then fuses all similarity matrices to obtain the final similarity matrix. Extensive experiments with real-life datasets on EA benchmarks offer insight into the proposed framework, and suggest that it is capable of outperforming the state-of-the-art scalable EA framework by up to 8 times in terms of Hits@1.
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