Top Experts Identification and Evaluation of International Cooperation on Artificial Intelligence in China

LIN Zhuo, HUANG Haohai

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Journal of Library and Information Sciences in Agriculture ›› 2022, Vol. 34 ›› Issue (1) : 86-95. DOI: 10.13998/j.cnki.issn1002-1248.21-0035

Top Experts Identification and Evaluation of International Cooperation on Artificial Intelligence in China

  • LIN Zhuo1,2, HUANG Haohai1
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Abstract

[Purpose/Significance] The paper aims to identify and evaluate the top experts ofinternational cooperation of artificial intelligence (AI) in China, so as to provide references for further international cooperation on AI in China. [Method/Process] The top AI experts whose H index is greater than 40are selected as the research object. Based on Aminer platform, this paper identifies top AI experts for international cooperation in China, and uses Topsis method to evaluate academic level of these experts. [Results/Conclusions] The results show that: (1) The cooperation of top experts between China and the United States is more frequently than that between China and other countries combined. (2) Top experts are mainly male, and the proportion of women is extremely low. (3) The institutions of top experts are distributed in 22 countries (regions) around the world, and the distribution of institutions is generally consistent with the situation of science & technology and economic strength of each region. (4) Some of top experts have the dual identities of university researchers and enterprise scientists. (5) Chinese and American top experts play a leading role in international cooperation in the field of academic papers. International migration of AI scientists reflects in Chinese scholars studying or working abroad, while the main international expert recruits in China are American experts. Meanwhile,although international AI cooperation has been fully carried out in China, it still has not established effective interflow and direct cooperation with some top experts, which include some leading AI experts.

Key words

artificial intelligence / top expert / international cooperation / expert identification / Aminer / Topsis

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LIN Zhuo, HUANG Haohai. Top Experts Identification and Evaluation of International Cooperation on Artificial Intelligence in China. Journal of Library and Information Sciences in Agriculture. 2022, 34(1): 86-95 https://doi.org/10.13998/j.cnki.issn1002-1248.21-0035

References

[1] 清华大学-中国工程院知识智能联合研究中心. 中国人工智能发展报告2019[R]. 北京: 清华大学, 2019.
Tsinghua university - Chinese academy of engineering knowledge intelligence joint research center. Report of artificial intelligence development (2019)[R]. Beijing: Tsinghua university, 2019.
[2] 高芳. 美国智库探讨人工智能与国家安全的关系[J]. 科技中国, 2018(1): 89-91.
GAO F. American think tank discusses the relationship between artificial intelligence and national security[J]. Science and technology China, 2018(1): 89-91
[3] 高芳, 耿喆. 特朗普启动人工智能倡议誓要维护全球领先地位[J]. 科技中国, 2019(5): 89-91.
GAO F, GENG Z. Trump launches AI initiative and vows to maintain global leading position[J]. Science and technology China, 2019(5): 89-91
[4] 常茹茹, 赵蓉英, 贾增帅, 等. 全球人工智能专利合作特征及影响力研究[J]. 农业图书情报学报, 2020, 32(2): 58-70.
CHANG R R, ZHAO R Y, JIA Z S, et al. The characteristics and impact of international artificial intelligence (AI) patent cooperation[J]. Agricultural library and information, 2020, 32(2): 58-70.
[5] 姜宇星, 王曰芬, 范丽鹏, 等. 人工智能研究前沿识别与分析: 基于主要国家(地区)对比研究视角[J]. 情报理论与实践, 2019, 42(9): 8-15.
JIANG Y X, WANG Y F, FAN L P, et al. Identification and analysis of research front in artificial intelligence: A perspective based on comparative study of major countries (regions)[J]. Intelligence theory and practice, 2019, 42(9): 8-15.
[6] 范丽鹏, 余厚强, 姜宇星, 等. 人工智能研究前沿识别与分析: 基于高产机构对比研究视角[J]. 情报理论与实践, 2019, 42(9): 16-21.
FAN L P, YU H Q, JIANG Y X, et al. Identification and analysis of research front in artificial intelligence: From a perspective of the comparative study among highly productive institutions[J]. Intelligence theory and practice, 2019, 42(9): 16-21
[7] 邹本涛, 王曰芬, 曹嘉君, 等. 人工智能研究前沿识别与分析: 基于高产作者多属性综合研究视角[J]. 情报理论与实践, 2019, 42(9): 22-27
ZOU B T, WANG Y F, CAO J J, et al. Identification and analysis of research front in artificial intelligence: A perspective based on multi-attribute comprehensive study of highly productive authors[J]. Intelligence theory and practice, 2019, 42(9): 22-27
[8] 中国新一代人工智能发展战略研究院. 中国新一代人工智能科技产业发展报告(2019)[R]. 天津: 中国新一代人工智能发展战略研究院, 2019.
Chinese institute of new generation artificial intelligence development strategies, openness and coordination: The impetus and mechanism of China's AI economy(2019)[R]. Tianjin: Chinese institute of new generation artificial intelligence development strategies, 2019.
[9] 孙坦, 黄永文, 鲜国建, 等. 新一代信息技术驱动下的农业信息化发展思考[J]. 农业图书情报学报, 2021, 33(3): 4-15.
SUN T, HUANG Y W, XIAN G J, et al. Considerations for the development of agricultural informatization driven by a new generation of information technologies[J]. Journal of library and information science in agriculture, 2021, 33(3): 4-15.
[10]TANG J, ZHANG J, YAO L, et al. Arnetminer: Extraction and mining of academic social networks[C]//Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, 2008: 990-998.
[11]TANG J, SUN J, WANG C, et al. Social influence analysis in large-scale networks[C]//Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, 2009: 807-816.
[12]TANG J, YAO L, ZHANG D, et al. A combination approach to web user profiling[J]. ACM transactions on knowledge discovery from data (TKDD), 2010, 5(1): 2.
[13]WANG Z, LI J, TANG J. Boosting cross-lingual knowledge linking via concept annotation[C]//Twenty-Third international joint conference on artificial intelligence, 2013.
[14]ZHANG Y, ZHANG F, YAO P, et al. Name disambiguation in AMiner: Clustering, maintenance, and human in the loop[C]//Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, ACM, 2018: 1002-1011.
[15]LI J, TANG J, LI Y, et al. Rimom: A dynamic multistrategy ontology alignment framework[J]. IEEE transactions on knowledge and data engineering, 2008, 21(8): 1218-1232.
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