Intelligent Bibliometrics: Models and Applications
Time: 13:00-15:00 p.m. (GMT+08:00) on Friday, October 7
Venue: Tencent Conference: 717-103-095
Reported by: Dr Yi Zhang, University of Science and Technology, Sydney
Introduction to the keynote speaker:
Dr. Zhang Yi is currently a senior lecturer (tenure) of the Australian Institute of Artificial Intelligence at the University of Science and Technology of Sydney, and a winner of the DECRA (Discovery Early Career Researcher Award) fund of the Australian Research Council in 2019. He holds double doctorates in management science and engineering (Beijing University of Technology) and software engineering (Sydney University of Science and Technology). He is a visiting scholar at the School of Public Policy of Georgia Institute of Technology (2011-2012).
Dr. Zhang Yi focuses on the research in the field of bibliometrics and technology innovation management, and emphasizes the theoretical framework and method innovation of intelligent literature intelligence for the management of technology innovation. More than 100 academic papers have been published (including 4 highly cited papers in 2017-2022). His Google Scholar paper has been cited more than 2100 times, with the H Index of 21.
Dr. Zhang Yi is now the deputy editor of the journal Technical Forecasting and Social Change and Scientology, the editorial board member of IEEE Transactions on Engineering Management, and the advisory member of the global committee of the Elsevier International Scientific Evaluation Center.
Introduction to the report:
Intelligent bibliometrics, highlighting the development and application of computational models incorporating AI and data science techniques with bibliographical information for broad studies in science, technology, and innovation scenarios. Its main tasks include topic extraction, relationship measurement and discovery, and prediction. Some representative works include embedding-based models for topic extraction and classification, heterogeneous network analytics for relationship discovery and prediction, etc. We have successfully applied intelligent bibliometrics to a wide range of ST&I scenarios, e.g., profiling large-scale coronavirus literature, discovering gene-disease associations, detecting emerging technologies, recommending knowledge trajectories of scientific researchers.
In this seminar, I will describe how my efforts take actions on recombining AI and data science with practical scenarios, problems, and issues, particularly in the case of bibliometrics and ST&I studies. I will showcase intelligent bibliometrics modelling through two cases: (1) Bi-layer bibliometric network analytics for characterising emerging general-purpose technologies; and (1) streaming data analytics-based analysis for monitoring topic disruption, evolution, and resilience in early COVID-19 crisis.
(Undertaken by: Knowledge Management and Data Analysis Laboratory, Scientific Research and Academic Exchange Center)
