KBGAN: Adversarial Learning for Knowledge Graph Embeddings理解
本篇論文的主要創新點在於將GANs與Knowledge graph embeddings(KGE)相結合，提高了KGE的效率。 傳統KGE方法通過隨機替換fact的head或tail entity生成負樣本，但這樣的負樣本往往與正樣本的語義差別較大，對模型的訓練沒有幫助。 因此，本文提出KBGAN——損失函數爲marginal loss function，帶有softmax的KGE模型。KBGAN
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相關文章
- 1. 論文理解—— DisenE: Disentangling Knowledge Graph Embeddings
- 2. Learning over Knowledge-Base Embeddings for Recommendation 論文
- 3. Towards Understanding the Geometry of Knowledge Graph Embeddings理解
- 4. Enriching BERT with Knowledge Graph Embeddings for Document Classification
- 5. PaperReading-TransR,《Learning Entity and Relation Embeddings for Knowledge Graph Completion
- 6. 論文筆記：AAAI 2020 Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
- 7. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 8. 關於《Domain Adaptation with Adversarial Training and Graph Embeddings》的理解
- 9. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
- 10. 論文解讀：Connecting Embeddings for Knowledge Graph Entity Typing