End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion--閱讀筆記
我的博客鏈接 0. 總覽 0.1 要解決什麼問題？（what？） 知識圖譜補全中： 以前的方法只建模關係三元組，忽略了大量圖節點相關屬性； 以前的方法並沒有增強在嵌入空間中大規模連接結構，完全忽略了圖結構。 0.2 用什麼方法解決？（how？） 用GCN和ConvErt相結合的方法組成一個端到端的學習方法。 encoder部分利用一個帶權重的GCN的方法（利用圖的結構並保留節點的屬性）。 deco
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相關文章
- 1. 【閱讀筆記】Reasoning With Neural Tensor Networks for Knowledge Base Completion
- 2. 《Knowledge Base Completion By Learning...》閱讀筆記
- 3. Side Information-Knowledge Graph Convolutional Networks for Recommender Systems閱讀筆記
- 4. Convolutional Neural Networks for Sentence Classification 閱讀筆記
- 5. Graph Convolutional Networks for Text Classification閱讀筆記
- 6. Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering讀書筆記
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- 9. Embedding Multimodal Relational Data for Knowledge Base Completion理解
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