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FlowNeg method enhances knowledge graph embedding with diverse negative sampling

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FlowNeg, a novel method for generating diverse and informative negative samples in knowledge graph embedding (KGE) models. This approach utilizes a context-conditioned hierarchical generative flow network to select entities that are both hard negatives and avoid collision with positive examples. Experiments across various architectures and benchmarks show FlowNeg outperforms existing methods like EMU and IF-NS in terms of mean reciprocal rank (MRR), demonstrating its effectiveness in improving KGE model learning. AI

IMPACT Enhances the learning efficiency and accuracy of knowledge graph embedding models by improving negative sampling strategies.

RANK_REASON The cluster contains a research paper detailing a new methodology for knowledge graph embedding. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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FlowNeg method enhances knowledge graph embedding with diverse negative sampling

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The cluster contains a research paper detailing a new methodology for knowledge graph embedding. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ibne Farabi Shihab, Naoshin Anzum Hridi, Joyanta Jyoti Mondal ·

    FlowNeg: GFlowNet-Guided Diverse Hard Negative Sampling for Knowledge Graph Embedding

    arXiv:2608.23849v1 Announce Type: new Abstract: Negative sampling determines whether a knowledge graph embedding (KGE) model learns from informative counterexamples or wastes updates on implausible corruptions. Uniform negatives are diverse but easy, whereas hard-negative miners …

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