论文题目: A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment
论文地址：https://www.ijcai.org/proceedings/2019/0574.pdf
研究动机
随着深度学习的发展，以图卷积网络（Graph Convolutional Network，GCN）[4]为代表的方法对图表示相关任务有很好的效果。然而，传统的基于频谱的GCN只能处理无向单关系网络，因为其要求归一化图拉普拉斯算子为实对称正半定矩阵，以便于进行图的傅立叶变换，这也表明邻接矩阵必须是对称的，并且二维的邻接矩阵也将边限制为相同类型，即单一关系。为了在图卷积中增加对多关系的支持，R-GCN[5]对每一关系学习一个映射矩阵，用于改变实体在累计邻居权重时考虑来自不同关系的影响，但R-GCN也没有显式地对关系进行表示。
a）显式的关系embedding学习。
b）实体角色区分：实体作为头实体或尾实体时采取不同的卷积操作，同时也体现图的有向性。
c）翻译模型的性质：学习到的表示具备形如TransE的h+r≈t的性质。
提出方法
1.VR-GCN框架
在VR-GCN的基础上，使用共享参数的设定分别训练两个输入的网络，同时增加网络对齐的目标，可以得到知识图谱对齐框架AVR-GCN。
实验分析
1.知识图谱对齐
评价指标使用的是MRR和Hits@k。两个指标越大，表明模型效果越好。
Baseline方面，使用了：将两个向量空间进行线性变换，从而达到对齐目的的MTransE[6]；迭代式增加训练数据的ITransE[7]；概率模型NTAM[8]；使用受限的Margin ranking loss的BootEA[9]的非迭代模型AlignE；基于图卷积网络的实体对齐模型GCN-Align[10]；以及AVR-GCN去除关系对齐目标的消融模型AVR-GCN(rl.exl.)。
可以看到AVR-GCN同样具有最好的效果。
这也符合我们的直观假设：训练数据越充分，模型效果越好。
数据集方面，使用的是链接预测任务广泛使用的公开数据集WN18和FB15k-237。
可以看到VR-GCN在提升网络表示效果上的提升还是很明显的。
IJCAI 2019涌现出很多实体对齐相关的文章，我们也将近年来基于表示学习的实体对齐方法做了整理，欢迎大家关注：https://github.com/THU-KEG/Entity_Alignment_Papers
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