基于注意力机制的多尺度空洞卷积神经网络模型

doi:10.16451/j.cnki.issn1003-6059.202106002

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Abstract The existing temporal knowledge graph representation methods cannot capture the complex relationships within quadruple well. Most of the neural network based models are unable to model time-varying knowledge and capture rich feature information. Moreover, the interaction between entities and relations in these models is poor. Therefore, a multi-scale dilated convolutional neural network model based on attention mechanism(MSDCA) is proposed. Firstly, a time-aware relation representation is obtained using long short-term memory. Secondly, a multi-scale dilated convolutional neural network is employed to improve the interactivity of the quadruple. Finally, a multi-scale attention mechanism is utilized to capture critical features to improve completion ability of MSDCA. Link prediction experiments on multiple public temporal datasets show the superiority of MSDCA.

Key words： Temporal Knowledge Graph Link Prediction Multi-scale Dilated Convolution Attention Mechanism

Received: 26 February 2021

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.61672159), Project in Industry-University Cooperation of Fujian

Corresponding Authors: WANG Jingbin, master, associate professor. Her research interests include knowledge graph, relation reasoning, distributed data management and knowledge representation.

About author:: LAI Xiaolian, master student. Her research interests include knowledge graph, relation reasoning and knowledge representation.
LEI Jing, master student. Her research interests include knowledge graph, relation reasoning and knowledge representation.
ZHANG Jingxuan, master student. Her research interests include knowledge graph, relation reasoning and knowledge representation.

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Cite this article:

WANG Jingbin,LAI Xiaolian,LEI Jing等. Multi-scale Dilated Convolutional Neural Network Model Based on Attention Mechanism[J]. , 2021, 34(6): 497-508.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202106002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I6/497

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