PaperReading-TransR,《Learning Entity and Relation Embeddings for Knowledge Graph Completion
本文記錄了閱讀TransR論文時的相關內容，如果有做相關工作的同學，歡迎與我溝通聯繫 [email protected] 背景 TransR論文的提出還是爲了來解決在TransE中無法解決的自反、一對多、多對一的關係問題。 有一個很顯然的想法就是，在知識圖譜中的實體和關係是不一樣，那麼他們存在的語義空間也就應該不一樣，實體空間就是專門的來表示實體上的屬性，關係空間就是來表示實體之間的關係內容的。
相關文章
- 1. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 2. KBGAN: Adversarial Learning for Knowledge Graph Embeddings理解
- 3. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs 閱讀筆記
- 4. 論文理解—— DisenE: Disentangling Knowledge Graph Embeddings
- 5. 2019_NAACL_A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Persona
- 6. Knowledge Graph Embedding: A Survey of Approaches and Applications 摘要
- 7. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs閱讀筆記
- 8. 論文筆記：ACL 2019 Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
- 9. Learning over Knowledge-Base Embeddings for Recommendation 論文
- 10. 論文解讀：Connecting Embeddings for Knowledge Graph Entity Typing
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相關文章
- 1. Transfer Learning for Item Recommendations and Knowledge Graph Completion
- 2. KBGAN: Adversarial Learning for Knowledge Graph Embeddings理解
- 3. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs 閱讀筆記
- 4. 論文理解—— DisenE: Disentangling Knowledge Graph Embeddings
- 5. 2019_NAACL_A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Persona
- 6. Knowledge Graph Embedding: A Survey of Approaches and Applications 摘要
- 7. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs閱讀筆記
- 8. 論文筆記：ACL 2019 Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs
- 9. Learning over Knowledge-Base Embeddings for Recommendation 論文
- 10. 論文解讀：Connecting Embeddings for Knowledge Graph Entity Typing