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
[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.
余传明,李浩男,王曼怡,黄婷婷,安璐. 基于深度学习的知识表示研究:网络视角*[J]. 数据分析与知识发现, 2020, 4(1): 63-75.
Chuanming Yu,Haonan Li,Manyi Wang,Tingting Huang,Lu An. Knowledge Representation Based on Deep Learning:Network Perspective. Data Analysis and Knowledge Discovery, 2020, 4(1): 63-75.
Perozzi B, Al-Rfou R, Skiena S. DeepWalk: Online Learning of Social Representations [C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM, 2014: 701-710.
[2]
Tang J, Qu M, Wang M, et al. LINE: Large-scale Information Network Embedding [C]// Proceedings of the 24th International Conference on World Wide Web. 2015: 1067-1077.
[3]
Grover A, Leskovec J. Node2Vec: Scalable Feature Learning for Networks [C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 855-864.
[4]
Wang D, Cui P, Zhu W. Structural Deep Network Embedding [C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM, 2016: 1225-1234.
( Zheng Yanning, Xu Xiaoyang, Liu Zhihui . Study on the Method of Identifying Research Fronts Based on Keywords Co-occurrence[J]. Library and Information Service, 2016,60(4):85-92.)
[6]
商宪丽, 王学东 . 图书情报知识[J]. 图书情报知识,2016(3):80-88.
[6]
( Shang Xianli, Wang Xuedong . A Feature Selection Method Based on Dynamic Co-Word Network for Microblog Topic Detection[J]. Document, Information & Knowledge, 2016(3):80-88.)
( Sun Yaowu, Gong Xiaoye . The Academic Interest of Technological Standardization Topic and Its Co-Word Network Evolution Research[J]. Journal of Intelligence, 2017,36(9):64-70, 37.)
( Ma Hong, Cai Yongming . A CA-LDA Model for Chinese Topic Analysis: Case Study of Transportation Law Literature[J]. New Technology of Library and Information Service, 2016(12):17-26.)
( Cai Yongming, Chang Qing . Chinese Short Text Topic Analysis by Latent Dirichlet Allocation Model with Co-Word Network Analysis[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(3):305-317.)
( Gao Jiping, Ding Kun, Pan Yuntao , et al. Importance Analysis and Application of Connections in Co-Word Networks[J]. Information Studies: Theory & Application, 2015,38(2):79-83,70.)
( Li Gang, Ren Jiajia, Mao Jin , et al. Analysis of the Community Structure of Patentees’ Collaboration Network——Fuel Cell Electric Vehicle Patents as an Example[J]. Journal of the China Society for Scientific and Technical Information, 2014,33(3):267-276.)
( Lv Penghui, Liu Shengbo . Scientific Knowledge Networks in LIS(IV): Investigation on the Structure and Characteristics of Cooperation Networks[J]. Journal of the China Society for Scientific and Technical Information, 2014,33(4):367-374.)
( Chen Wei, Li Chuanyun, Zhou Wen , et al. Research on the Weighted Patent Cooperation Network Based on New Energy Vehicles[J]. Journal of the China Society for Scientific and Technical Information, 2016,35(6):563-572.)
( Fan Ruxia, Zeng Jianxun, Gao Yaruixi . Recognizing Dynamic Academic Impacts of Scholars Based on Cooperative Network[J]. Data Analysis and Knowledge Discovery, 2017,1(4):30-37.)
( Shi Xiaohua, Lu Hongtao . Detecting Community in Scientific Collaboration Network with Bayesian Symmetric NMF[J]. Data Analysis and Knowledge Discovery, 2017,1(9):49-56.)
( Lv Penghui, Zhang Shijing . Scientific Knowledge Networks in LIS(I): Case Study on the Structure, Characteristics and Evolution of Citation Networks[J]. Journal of the China Society for Scientific and Technical Information, 2014,33(4):340-348.)
( Wei Ling, Liu Chunjiang, Xu Haiyun , et al. Citation Network Main Path Identification Based on Associated Attributes of Articles: Case Study from Synthetic Biology[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(4):351-361.)
( Wang Zhongyi, Zhang Heming, Huang Jing , et al. Studying Knowledge Dissemination of Online Q&A Community with Social Network Analysis[J]. Data Analysis and Knowledge Discovery, 2018,2(11):80-94.)
( Fan Xinyue, Cui Lei . Predicting Antineoplastic Drug Targets Based on Network Properties[J]. Data Analysis and Knowledge Discovery, 2018,2(12):98-108.)
( Yu Chuanming, Feng Bolin, An Lu . Sentiment Analysis in Cross-Domain Environment with Deep Representative Learning[J]. Data Analysis and Knowledge Discovery, 2017,1(7):73-81.)
( Li Yuqi, Chen Weizheng, Yan Hongfei , et al. Learning Graph-based Embedding for Personalized Product Recommendation[J]. Chinese Journal of Computers, 2019,42(8):1767-1778.)
( Zhang Jinzhu, Yu Wenqian, Liu Jingjie , et al. Predicting Research Collaborations Based on Network Embedding[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(2):132-139.)
( Liu Shuwen, Xu Yang, Wang Binglu , et al. Water Army Detection of Weibo Using User Representation Learning[J]. Journal of Intelligence, 2018,37(7):95-100,87.)
[25]
樊玮, 韩佳宁, 张宇翔 . 基于网络表示学习的论文影响力预测算法[J/OL]. 计算机工程. .
[25]
( Fan Wei, Han Jianing, Zhang Yuxiang . Paper Influence Prediction Algorithm Based on Network Representation Learning[J/OL]. Computer Engineering. .)
( Sun Xiaoling, Ding Kun . Study of Representation Learning in Deep Learning and Its Impact on Knowledge Measurement[J]. Information Studies: Theory & Application, 2018,41(9):118-122.)
( Liao Xiangwen, Liu Deyuan, Gui Lin , et al. Opinion Retrieval Method Combining Text Conceptualization and Network Embedding[J]. Journal of Software, 2018,29(10):2899-2914.)
( Liu Si, Liu Hai, Chen Qimai , et al. Link Prediction Algorithm Based on Network Representation Learning and Random Walk[J]. Journal of Computer Applications, 2017,37(8):2234-2239.)
( Liu Zhiyuan, Sun Maosong, Lin Yankai , et al. Knowledge Representation Learning: A Review[J]. Journal of Computer Research and Development, 2016,53(2):247-261.)