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Global Research on Artificial Intelligence from 1990–2014

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Global Research on Artificial Intelligence from 1990–2014 International Journal of Geo-Information Review Global Research on Artificial Intelligence from 1990–2014: Spatially-Explicit Bibliometric Analysis Jiqiang Niu 1,*, Wenwu Tang 2,3, Feng Xu 1, Xiaoyan Zhou 4 and Yanan Song 4 1 School of Urban and Environmental Science, Xinyang Normal University, Xinyang 464000, China; [email protected] 2 Department of Geography and Earth Sciences, University of North Carolina at Charlotte, Charlotte, NC 28262, USA; [email protected] 3 Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC 28262, USA 4 School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China; [email protected] (X.Z.); [email protected] (Y.S.) * Correspondence: [email protected]; Tel.: +86-188-3767-2366 Academic Editors: Kathleen Stewart, Alexander Klippel and Wolfgang Kainz Received: 5 February 2016; Accepted: 10 May 2016; Published: 16 May 2016 Abstract: In this article, we conducted the evaluation of artificial intelligence research from 1990–2014 by using bibliometric analysis. We introduced spatial analysis and social network analysis as geographic information retrieval methods for spatially-explicit bibliometric analysis. This study is based on the analysis of data obtained from database of the Science Citation Index Expanded (SCI-Expanded) and Conference Proceedings Citation Index-Science (CPCI-S). Our results revealed scientific outputs, subject categories and main journals, author productivity and geographic distribution, international productivity and collaboration, and hot issues and research trends. The growth of article outputs in artificial intelligence research has exploded since the 1990s, along with increasing collaboration, reference, and citations. Computer science and engineering were the most frequently-used subject categories in artificial intelligence studies. The top twenty productive authors are distributed in countries with a high investment of research and development. The United States has the highest number of top research institutions in artificial intelligence, producing most single-country and collaborative articles. Although there is more and more collaboration among institutions, cooperation, especially international ones, are not highly prevalent in artificial intelligence research as expected. The keyword analysis revealed interesting research preferences, confirmed that methods, models, and application are in the central position of artificial intelligence. Further, we found interesting related keywords with high co-occurrence frequencies, which have helped identify new models and application areas in recent years. Bibliometric analysis results from our study will greatly facilitate the understanding of the progress and trends in artificial intelligence, in particular, for those researchers interested in domain-specific AI-driven problem-solving. This will be of great assistance for the applications of AI in alternative fields in general and geographic information science, in particular. Keywords: Artificial Intelligence; bibliometric analysis; scientific outputs; research trends; SCI-expanded; Conference Proceedings Citation Index-Science 1. Introduction Artificial Intelligence (AI) is a subject that studies theories, methods, and applications with respect to simulation, extension, and expansion of human intelligence for problem-solving. Application domains of AI include robotics, voice recognition, image recognition, natural language processing, and ISPRS Int. J. Geo-Inf. 2016, 5, 66; doi:10.3390/ijgi5050066 www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2016, 5, 66 2 of 18 ISPRS Int. J. Geo-Inf. 2016, 5, 66 2 of 19 essence of intelligence and design intelligent machines that can act as human behavior. AI has expertattracted systems researchers [1]. AI, aswith a branch respect of to computer its theories science, and principles aims to understand since the 1956 the essence Dartmouth of intelligence conference and[2,3 design]. Since intelligent the 1960s, machinesAI-related that researches can act ashave human been behavior.heavily funded, AI has and attracted many researchers laboratories with have respectbeen established to its theories around and the principles world. However, since the in 1956 the mid Dartmouth-1970s and conference early 1980s, [2, 3there]. Since are two the periods 1960s, AI-relatedof stagnancy researches in AI [ have4]. Until been the heavily 1990s, funded, AI achieved and many its greatest laboratories success, have which been can established be confirmed around by thethe world. scientific However, articles in indexed the mid-1970s by the Science and early Citation 1980s, Index there Expanded are two periods (SCI-Expanded of stagnancy) (Figure in AI 1 [4).]. It Untilis during the 1990s, this period AI achieved that AIits is widely greatest used success, for logistics, which can data be mining, confirmed medical by the diagnosis, scientific and articles many indexedother areas by the throughout Science Citation industry Index [2,5] Expanded. From the (SCI-Expanded)early 21st century, (Figure AI research1). It is duringenters a this period period with thatnumerous AI is widely research used foroutputs. logistics, The data success mining, medicalwas attr diagnosis,ibuted to and several many otherfactors: areas the throughout increasing industrycomputational [2,5]. From power the earlyof computers, 21st century, a greater AI research emphasis enters aon period problem with-solving, numerous creation research of outputs.new ties Thebetween success AI was and attributed other fields to working several factors:on similar the problems, increasing and computational a new commitment power ofby computers, researchers ato greatersolid mathematical emphasis on problem-solving,methods and rigorous creation scientific of new standards ties between [6,7 AI]. Today, and other AI and fields its working applications on similarplay an problems, important and role a new in computer commitment science by researchersand its related to solid domains mathematical [8]. methods and rigorous scientific standards [6,7]. Today, AI and its applications play an important role in computer science and its related domains [8]. Figure 1. Growth trend of articles in artificial intelligence from 1990–2014. Figure 1. Growth trend of articles in artificial intelligence from 1990–2014. Bibliometric analysis comprises a series of quantitative and visual procedures or statistics to generalizeBibliometric research patterns,analysis dynamicscomprises anda series trends of in quantitative scientific publication and visual [9– 12procedures]. In recent or years, statistics many to bibliometricgeneralize research methods patterns, were put dynamics forward toand estimate trends in the scientific scientific publication outputs or [9 research–12]. In recent trend ofyears, authors, many journals,bibliometric institutions, methods and were countries, put forward even to to estimate identify andthe scientific quantify outputs international or research collaboration trend of [ 13authors,–15]. Asjournals, the effective institutions, method and for countries, providing even an immediate to identify pictureand quantify of the actualinternational content collaboration of research topics,[13–15]. citationAs the analysis,effective co-wordsmethod for analysis, providing and an geographic immediate impact picture factor of the (GIF) actual were content carried of outresearch within topics, the fieldcitation of bibliometric analysis, co analysis-words analysis to enhance, and thegeographic conventional impact bibliometric factor (GIF) methods were carried [16–19 out]. Recently,within the correlationfield of bibliometric analysis was analysis introduced to enhance in bibliometrics the conventional to explain thebibliometric phenomenon methods of research [16–19 trends]. Recently, [15]. correlationAI is a research analysis domainwas introduced that includes in bibliomet many branchesrics to explain and methods.the phenomenon While bibliographic of research trends data for[15]. AI-relatedAI is studies a research are domain increasingly that includes available, many how branches to discover and the methods. research While patterns bibliographic and trends data in AI for basedAI-related on these studies bibliographic are increasingly data represents available, a challenginghow to discover research the research question. patterns Further, and bibliographic trends in AI databased are on spatially these bibliographic explicit per se. dataHowever, represents there a is challenging a gap between research the use question of spatially-explicit. Further, bibliographic methods fordata analyzing are spatially these explicit bibliographic per se. However, data (i.e., there retrieval is a gap of geographic between the information use of spatially from-explicit bibliometrics) methods andforadvances analyzing in these Geographic bibliographic Information data Science(i.e., retrieval (GIScience; of geographic [20]), which information focuses on from the developmentbibliometrics) ofand these advances spatially-explicit in Geographic methods. Information Therefore, Science in (GIScience this study; we[20] conduct), which focuses a bibliometric on the development analysis of AIof research these spatially for the-explicit period ofmethods 1990–2014.. Therefore, We proposed in this study an alternative we conduct and a innovativebibliometric method, analysis with of AI supportresearch from for geographicthe period o informationf 1990–2014. retrieval We proposed technologies, an alternative
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