KBGAN: Adversarial Learning for Knowledge Graph Embeddings理解
本篇论文的主要创新点在于将GANs与Knowledge graph embeddings(KGE)相结合，提高了KGE的效率。 传统KGE方法通过随机替换fact的head或tail entity生成负样本，但这样的负样本往往与正样本的语义差别较大，对模型的训练没有帮助。 因此，本文提出KBGAN——损失函数为marginal loss function，带有softmax的KGE模型。KBGAN
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相关文章
- 1. 论文理解—— DisenE: Disentangling Knowledge Graph Embeddings
- 2. Learning over Knowledge-Base Embeddings for Recommendation 论文
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- 4. Enriching BERT with Knowledge Graph Embeddings for Document Classification
- 5. PaperReading-TransR,《Learning Entity and Relation Embeddings for Knowledge Graph Completion
- 6. 论文笔记：AAAI 2020 Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
- 7. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 8. 关于《Domain Adaptation with Adversarial Training and Graph Embeddings》的理解
- 9. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
- 10. 论文解读：Connecting Embeddings for Knowledge Graph Entity Typing