Knowledge Reasoning Based on Neural Tensor Network

被引：1

作者：

Huang, Jian-Hui ^{[1
]}

Huang, Jiu-Ming ^{[1
]}

Li, Ai-Ping ^{[1
]}

Tong, Yong-Zhi ^{[1
]}

机构：

[1] Natl Univ Def Technol, Mass Data Proc Lab, Changsha, Hunan, Peoples R China

来源：

4TH ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND APPLICATIONS (ITA 2017) | 2017年 / 12卷

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1051/itmconf/20171204004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Knowledge base (KBs) is a very important part of applications such as Q&A system, but the knowledge base is always faced with incompleteness and the lack of inter-entity relationships. Knowledge reasoning is an important part of the construction of knowledge base, and is intended to find a way to supplement these missing relationships. This paper attempts to explore the model complexity of neural tensor network, a very important method of knowledge reasoning, and the reasoning accuracy. By increasing the number of slices in the tensor network layer, the number of parameters to be trained by the model is increased, thereby increasing the complexity of the model. The experimental results show that the number of slices is improved, which is helpful to increase the reasoning accuracy of the model, while the time consumption does not show obvious growth. The accuracy of the model on WordNet and FreeBase increased 2% and 3.2% respectively.

引用

页数：5

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