实体对齐4.EMNLP2018：（GCN-Align）Cross-lingual Knowledge Graph Alignment via Graph Convolutional Networks
关键词：Cross-lingual Entity Alignment，GCN，Embedding 摘要 多语言知识图谱（KG）例如DBpedia和YAGO都包含几种不同语言实体中的机构性知识，并且它们对于跨语言AI和NLP应用都是有用的资源。跨语言KG对齐是一个将实体与它们在不同语言中对应部分匹配起来的任务，这是丰富多语言KG的跨语言链接的重要途径。本文中，我们提出一种基于图卷积神经网络（GCN）
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相关文章
- 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 解读
- 8. Adaptive Graph Convolutional Neural Networks
- 9. 实体对齐2.IJCAI 2017：（IPTransE）Iterative Entity Alignment via Joint Knowledge Embeddings
- 10. Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification