MultiKE: a Multi-view Knowledge Graph Embedding Framework for Entity Alignment
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We study the problem of embedding-based entity alignment (EA) between knowledge graphs (KGs), and propose a novel framework that unifies multiple views of entities to learn their embeddings. Experiments on real-world datasets show that this framework largely outperforms the current embedding-based methods.
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