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.
Highlights
Artificial intelligence is considered as a cutting-edge technology that is increasingly driv‐ ing developments and innovations in a wide range of scientific, technological, business, and government fields (WIPO 2019a)
If greater than 50% of the sample comprised publications relevant to artificial intelligence, the candidate keyword was included in our final search strategy, deeming this candidate keyword as having a low noise ratio (LR)
LR represents “Low noise ratio”, with more than 50% of the 25-record random sample falling in the area of (B not A ∩ B) relevant artificial intelligence records
Summary
Artificial intelligence is considered as a cutting-edge technology that is increasingly driv‐ ing developments and innovations in a wide range of scientific, technological, business, and government fields (WIPO 2019a). A varia‐ tion is an evolutionary lexical query with semi-automated iteration, for example by iden‐ tifying core publications in an emerging field with a simple search strategy, identifying keywords and their frequency rank, repeating the search with highly-ranked keywords until convergence and involving experts in reviewing expanded keyword groups This method still relies on the reliability of keyword selection and expert input (Huang et al 2011, 2015). Our search approach seeks to capture publications clearly acknowledged as artificial intelligence and pub‐ lications that should be included in the artificial intelligence field, even though their titles, abstracts or keywords may not involve the core term “artificial intelligence”. We use the core lexical query “artificial intelligence” as a topic search as well as a query of specialized artificial intelligence journals as a source search From these benchmark records, we extract “Author Keywords” and “Keywords Plus” and derive the frequencies of these keywords. This process allowed us to identify nine keywords as core lexical because they frequently co-occurred
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