ClusterEA: Scalable Entity Alignment with Stochastic Training and Normalized Mini-batch Similarities. (arXiv:2205.10312v1 [cs.DB])

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 to
EA. However, the increasing scale of KGs renders it is hard for EA models to
adopt the normalization processes, thus limiting their usage in real-world
applications. To tackle …

alignment arxiv scalable stochastic training