The content delves into the importance of negative sampling techniques in KGRL for generating high-quality samples. It discusses various methods like Random, Probabilistic, External Model-Based, and Auxiliary Data-Based NS. The article highlights the pros and cons of each approach and their impact on efficiency, effectiveness, stability, independence, and quality.
The review covers a range of negative sampling strategies such as Uniform, Bernoulli, Nearest Neighbor (NN), Adaptive Negative Sampling (ANS), Entity-aware Negative Sampling (EANS), ϵ-Truncated UNS, Truncated NS, Distributional Negative Sampling (DNS), among others. Each method is analyzed based on its efficiency, effectiveness, stability, independence from side information, and quality of generated negatives.
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by Tiroshan Mad... at arxiv.org 03-01-2024
https://arxiv.org/pdf/2402.19195.pdfDeeper Inquiries