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追踪人工智能研究的发展:构建并应用一种新的搜索策略。

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

作者信息

Liu Na, Shapira Philip, Yue Xiaoxu

机构信息

School of Management, Shandong Technology and Business University, Yantai, 264005 China.

Manchester Institute of Innovation Research, Alliance Manchester Business School, University of Manchester, Manchester, M13 9PL UK.

出版信息

Scientometrics. 2021;126(4):3153-3192. doi: 10.1007/s11192-021-03868-4. Epub 2021 Feb 25.

Abstract

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.

摘要

人工智能作为一个新兴的多学科研究与创新领域,近年来受到了越来越多的关注。描绘人工智能的领域构成对于剖析和追踪其发展及轨迹至关重要。本文提出了一个易于应用的人工智能文献计量学定义,研究人员、管理人员和政策分析师均可使用。我们的方法始于通过核心关键词和专业期刊搜索获取的人工智能基准记录。然后,我们从基准记录的高频关键词中提取候选术语,完善关键词并辅以“人工智能”主题类别。我们通过与最近其他三种人工智能搜索策略进行比较,使用来自《科学引文索引》的共同文章来源来评估我们的搜索方法。利用该来源,我们描绘了近年来人工智能科学研究的增长模式和国际传播情况,确定了资助人工智能的顶级研究赞助商,并展示了不同学科如何为人工智能的多学科发展做出贡献。我们最后得出了对搜索策略发展的启示以及进一步研究方向的建议。

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