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Negative Sampling in Knowledge Graph Representation Learning: A Review
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Knowledge Graph Representation Learning (KGRL), or Knowledge Graph Embedding (KGE), is essential for AI applications such as knowledge construction and information retrieval. These models encode entities and relations into lower-dimensional vectors, supporting tasks like link prediction and recommendation systems. Training KGE models relies on both positive and negative samples for effective learning, but generating high-quality negative samples from existing knowledge graphs is challenging. The quality of these samples significantly impacts the model's accuracy. This comprehensive survey paper systematically reviews various negative sampling (NS) methods and their contributions to the success of KGRL. Their respective advantages and disadvantages are outlined by categorizing existing NS methods into six distinct categories. Moreover, this survey identifies open research questions that serve as potential directions for future investigations. By offering a generalization and alignment of fundamental NS concepts, this survey provides valuable insights for designing effective NS methods in the context of KGRL and serves as a motivating force for further advancements in the field.
Forward citations
Cited by 4 Pith papers
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Diffusion-based Hierarchical Negative Sampling for Multimodal Knowledge Graph Completion
A diffusion-based hierarchical negative sampling framework with hardness-adaptive training improves multimodal knowledge graph completion on three benchmarks.
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Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion
Booster, a plug-in augmentation for temporal knowledge graph completion, adds pattern-scored missing facts as training data and fine-tunes on hard examples, yielding up to 8.7% MRR gains.
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Enhancing PyKEEN with Multiple Negative Sampling Solutions for Knowledge Graph Embedding Models
A PyKEEN extension integrates seven known negative sampling strategies; experiments on FB15K and WN18 show pool sizes shrink sharply and random fallback often dominates at high sample counts.
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Domain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models
Mixing type-constrained and random negative sampling improves KGE link prediction on FB15k-237 and Hetionet for all four tested models, but gives near-zero or negative gains on WN18RR.
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