Tracking developments in artificial intelligence research: constructing and applying a new search strategy

被引:41
作者
Liu, Na [1 ]
Shapira, Philip [2 ,3 ]
Yue, Xiaoxu [4 ]
机构
[1] Shandong Technol & Business Univ, Sch Management, Yantai 264005, Peoples R China
[2] Univ Manchester, Alliance Manchester Business Sch, Manchester Inst Innovat Res, Manchester M13 9PL, Lancs, England
[3] Georgia Inst Technol, Sch Publ Policy, Atlanta, GA 30332 USA
[4] Tsinghua Univ, Sch Publ Policy & Management, Beijing 100084, Peoples R China
基金
英国生物技术与生命科学研究理事会; 中国国家自然科学基金;
关键词
Emerging technology; Artificial intelligence; Bibliometric analysis; Search strategy; Research trends; SCIENTIFIC-RESEARCH; TECHNOLOGY; SCIENCE; EMERGENCE; AI;
D O I
10.1007/s11192-021-03868-4
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Artificial intelligence, as an emerging and multidisciplinary domain of research and innovation, has attracted growing attention in recent years. Delineating the domain composition of artificial intelligence is central to profiling and tracking its development and trajectories. This paper puts forward a bibliometric definition for artificial intelligence which can be readily applied, including by researchers, managers, and policy analysts. Our approach starts with benchmark records of artificial intelligence captured by using a core keyword and specialized journal search. We then extract candidate terms from high frequency keywords of benchmark records, refine keywords and complement with the subject category "artificial intelligence". We assess our search approach by comparing it with other three recent search strategies of artificial intelligence, using a common source of articles from the Web of Science. Using this source, we then profile patterns of growth and international diffusion of scientific research in artificial intelligence in recent years, identify top research sponsors in funding artificial intelligence and demonstrate how diverse disciplines contribute to the multidisciplinary development of artificial intelligence. We conclude with implications for search strategy development and suggestions of lines for further research.
引用
收藏
页码:3153 / 3192
页数:40
相关论文
共 62 条
  • [21] Feldstein Steven, 2019, GLOBAL EXPANSION SUR
  • [22] Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018
    Gao, Jie
    Huang, Xinping
    Zhang, Lili
    [J]. SUSTAINABILITY, 2019, 11 (23)
  • [23] Glänzel W, 2004, HANDBOOK OF QUANTITATIVE SCIENCE AND TECHNOLOGY RESEARCH: THE USE OF PUBLICATION AND PATENT STATISTICS IN STUDIES OF S&T SYSTEMS, P257
  • [24] Glanzel W., 2019, Springer Handbook of Science and Technology Indicators
  • [25] Measuring scientific research in emerging nano-energy field
    Guan, Jiancheng
    Liu, Na
    [J]. JOURNAL OF NANOPARTICLE RESEARCH, 2014, 16 (04)
  • [26] An index to quantify an individual's scientific research output
    Hirsch, JE
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2005, 102 (46) : 16569 - 16572
  • [27] Nanoscience and technology publications and patents: a review of social science studies and search strategies
    Huang, Can
    Notten, Ad
    Rasters, Nico
    [J]. JOURNAL OF TECHNOLOGY TRANSFER, 2011, 36 (02) : 145 - 172
  • [28] A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for 'Big Data'
    Huang, Ying
    Schuehle, Jannik
    Porter, Alan L.
    Youtie, Jan
    [J]. SCIENTOMETRICS, 2015, 105 (03) : 2005 - 2022
  • [29] Jackson P. C., 2019, INTRO ARTIFICIAL INT
  • [30] Artificial intelligence, machine learning and deep learning: definitions and differences
    Jakhar, D.
    Kaur, I.
    [J]. CLINICAL AND EXPERIMENTAL DERMATOLOGY, 2020, 45 (01) : 131 - 132