Heterogeneous Knowledge Learning of Predictive Academic Intelligence in Transportation

doi:10.1109/TITS.2020.3041904

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Heterogeneous Knowledge Learning of Predictive Academic Intelligence in Transportation
	Lu, Hao 4,5; Zhu, Yifan 5; Lin, Qika 6; Wang, Tan 7,8; Niu, Zhendong 2,5; Herrera-Viedma, Enrique 1,3
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2022-04-01
卷号	23 期号:4 页码:3737-3755
关键词	Academic impact prediction predictive knowledge analytics transportation research heterogeneous knowledge learning academic intelligence social transportation
ISSN号	1524-9050
DOI	10.1109/TITS.2020.3041904
通讯作者	Niu, Zhendong(zniu@bit.edu.cn)
英文摘要	The widespread communication of academic ideas and research achievements through literature and media has generated massive academic big data. Analyzing such academic big data and discovering knowledge can discover comprehensive and predictive academic intelligence and provide corresponding services, which are valuable for scholars, journals, institutions and governments for career assessment, topic selection, funding management and resource allocation. This paper proposes a heterogeneous knowledge-learning method for understanding and predicting academic impact in the transportation field. The proposed method is illustrated on the academic big data collected from the papers published in 34 transportation journals from 2008 to 2018. We extract four types of features including bibliometric, altmetric, network and semantic features, and build hybrid feature embedding via TransR and Doc2vec that involving domain knowledge. Further, an academic impact prediction model for articles named as Hy-LSTM-Att is proposed, which weighs the hybrid features by the attention mechanism and predicts academic impact with the bi-LSTM recurrent neural networks. Experimental results demonstrate that the proposed Hy-LSTM-Att model outperforms competing shallow and deep learning models. Additionally, the feature ablation experiments illustrate that the four types of features positively contribute to the performance of impact prediction.
资助项目	National Key Research and Development Program of China[2019YFB1406302] ; National Key Research and Development Program of China[2018AAA0101502] ; National Key Research and Development Program of China[2018YFB1702701] ; National Key Research and Development Program of China[2018YFB1700202] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[61370137] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[61702519] ; National Natural Science Foundation of China[61773381] ; National Natural Science Foundation of China[61773382] ; National Natural Science Foundation of China[U1909204]
WOS关键词	CITATION COUNTS ; IEEE TRANSACTIONS ; PARALLEL CONTROL ; SYSTEMS ; IMPACT ; NETWORK ; ARTICLES ; LEVEL
WOS研究方向	Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000776187400069
资助机构	National Key Research and Development Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48237]
专题	自动化研究所_中国科学院分子影像重点实验室
通讯作者	Niu, Zhendong
作者单位	1.Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada 18071, Spain 2.Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA 15260 USA 3.King Abdulaziz Univ, Fac Engn, Dept Elect & Comp Engn, Jeddah 80200, Saudi Arabia 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China 6.Xi An Jiao Tong Univ, Sch Comp Sci & Technol, Xian 710049, Peoples R China 7.Chinese Acad Sci, Inst Automat, Chinese Assoc Automat, Beijing 100190, Peoples R China 8.Macau Univ Sci & Technol, Inst Syst Engn, Taipa 999078, Macao, Peoples R China
推荐引用方式 GB/T 7714	Lu, Hao,Zhu, Yifan,Lin, Qika,et al. Heterogeneous Knowledge Learning of Predictive Academic Intelligence in Transportation[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022,23(4):3737-3755.
APA	Lu, Hao,Zhu, Yifan,Lin, Qika,Wang, Tan,Niu, Zhendong,&Herrera-Viedma, Enrique.(2022).Heterogeneous Knowledge Learning of Predictive Academic Intelligence in Transportation.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,23(4),3737-3755.
MLA	Lu, Hao,et al."Heterogeneous Knowledge Learning of Predictive Academic Intelligence in Transportation".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 23.4(2022):3737-3755.