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陈文杰; 文奕; 张鑫; 杨宁; 赵爽
Source Publication计算机工程


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Since the distance-based model TransE has been proposed, a series of models try to improve TransE, such as TransH and TransG. But distance-based models could not deal well with one-to-many, many-to-one, and many-to-many relationships, and often isolated learning triples do not take into account the network structure and semantic information of the knowledge graph. TransGraph based on TransE ,which simultaneously learn triples and knowledge graph network structure. In order to realize the deep fusion of network structure information and triplet information, a vector-sharing cross-training mechanism is proposed. The results show that TransGraph has achieved significant improvements in link prediction and triplet classification compared to the TransE.

Keyword知识图谱 表示学习 transe Transgraph 神经网络
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Document Type期刊论文
First Author Affilication中国科学院文献情报中心
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GB/T 7714
陈文杰,文奕,张鑫,等. 一种改进的基于TransE的知识图谱表示方法[J]. 计算机工程,2019,3(23):14.
APA 陈文杰,文奕,张鑫,杨宁,&赵爽.(2019).一种改进的基于TransE的知识图谱表示方法.计算机工程,3(23),14.
MLA 陈文杰,et al."一种改进的基于TransE的知识图谱表示方法".计算机工程 3.23(2019):14.
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