Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (1): 63-75    DOI: 10.11925/infotech.2096-3467.2019.0505
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Knowledge Representation Based on Deep Learning:Network Perspective
Chuanming Yu1(),Haonan Li2,Manyi Wang2,Tingting Huang2,Lu An3
1School of Information and Security Engineering, Zhongnan University of Economics and Law,Wuhan 430073, China
2School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, China
3School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract

[Objective] This paper explores better representation models for the semantic relationship among knowledge objects.[Methods] Based on the existing algorithm of network representation learning, we proposed a combined knowledge network representation learning model (CKNRL), with integrated learning and deep learning techniques.[Results] We examined our new model with the knowledge network link prediction task of Chinese and English news parallel corpus. The AUC value of the CKNRL model was 0.929, which was higher than those of the traditional algorithms, i.e. DeepWalk(0.925), Node2Vec(0.926) and SDNE(0.899).[Limitations] Our study was based on the word co-occurrence network, and more research is needed to examine the CKNRL model for link prediction on more types of knowledge networks.[Conclusions] The semantic relationship among knowledge objects can be better represented by the proposed fusion model.

Received: 14 May 2019      Published: 14 March 2020
 ZTFLH: TP391
Corresponding Authors: Chuanming Yu     E-mail: yucm@zuel.edu.cn