知识图谱与图神经网络_biji
图示化表示学习则聚焦于对图形数据特性的处理。相比于知识图谱嵌入模型与基于规则的知识提取技术而言，在语义与逻辑特性方面具有更强的关注点。更为优质的知识图谱表示技术应当通过综合运用语义特性、逻辑关系特性和图形架构特性的提取与建模方法来实现对知识的有效组织与表达
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RGCN: Establishing Relationships in Data through Graph Convolutional Networks. (ESWC 2018).
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COMPGCN: Compositional Multi-Relation Graph Convolutional Networks. (ICLR 2020).
Transferring Knowledge Beyond the Known Knowledge Base: A Graph Neural Network Approach.(IJCAI 2017) -
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks. (NAACL 2019).
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RDGCN: Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs. (IJCAI 2019).
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Commonsense Knowledge Aware Conversation Generation with Graph Attention. (IJCAI 2018).
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End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion. (AAAI 2019).
GCN 和 ConvE 的结合，整体结构是encoder-decoder。带权重的GCN学习entity 表示，TransE学习relation 表示。将entity 表示和relation表示作为encoder的中间结果，作为decoder 的输入。
