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How Many and How Much? Contrastive Learning with Negative Samples for Knowledge Graph Embedding
Knowledge graph embedding (KGE), a fundamental technique for knowledge-centric Web and KG-augmented AI applications, is commonly trained through contrastive learning with negative samples to learn discriminative representations. Despite its empirical success, two crucial questions regarding negative
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