實體對齊4.EMNLP2018：（GCN-Align）Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
關鍵詞：Cross-lingual Entity Alignment，GCN，Embedding 摘要 多語言知識圖譜（KG）例如DBpedia和YAGO都包含幾種不同語言實體中的機構性知識，並且它們對於跨語言AI和NLP應用都是有用的資源。跨語言KG對齊是一個將實體與它們在不同語言中對應部分匹配起來的任務，這是豐富多語言KG的跨語言鏈接的重要途徑。本文中，我們提出一種基於圖卷積神經網絡（GCN）
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相關文章
- 1. Knowledge Graph Convolutional Networks for Recommender Systems
- 2. 二Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network
- 3. EMNLP2019 | Knowledge-Aware Graph Networks
- 4. Cross-lingual Knowledge Graph Alignment viaGraph Matching Neural Network
- 5. Graph Few-shot learning via Knowledge Transfer
- 6. MuchGCN：Multi-Channel Graph Convolutional Networks
- 7. convolutional 2D knowledge graph embedding 解讀
- 8. Adaptive Graph Convolutional Neural Networks
- 9. 實體對齊2.IJCAI 2017：（IPTransE）Iterative Entity Alignment via Joint Knowledge Embeddings
- 10. Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification