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Principled Representation Learning for Entity Alignment
[article]
2021
arXiv
pre-print
Embedding-based entity alignment (EEA) has recently received great attention. Despite significant performance improvement, few efforts have been paid to facilitate understanding of EEA methods. Most existing studies rest on the assumption that a small number of pre-aligned entities can serve as anchors connecting the embedding spaces of two KGs. Nevertheless, no one investigates the rationality of such an assumption. To fill the research gap, we define a typical paradigm abstracted from
arXiv:2110.10871v1
fatcat:gca53at2abbvrbum2guj7r63xa