Profiling and predicting the problem-solving patterns in China’s research systems: A methodology of intelligent bibliometrics and empirical insights

Abstract

Uncovering the driving forces, strategic landscapes, and evolutionary mechanisms of China’s research systems is attracting rising interest around the globe. One topic of interest is to understand the problem-solving patterns in China’s research systems now and in the future. Targeting a set of high-quality research articles published by Chinese researchers between 2009 and 2018, and indexed in the Essential Science Indicators database, we developed an intelligent bibliometrics-based methodology for identifying the problem-solving patterns from scientific documents. Specifically, science overlay maps incorporating link prediction were used to profile China’s disciplinary interactions and predict potential cross-disciplinary innovation at a macro level. We proposed a function incorporating word embedding techniques to represent subjects, actions, and objects (SAO) retrieved from combined titles and abstracts into vectors and constructed a tri-layer SAO network to visualize SAOs and their semantic relationships. Then, at a micro level, we developed network analytics for identifying problems and solutions from the SAO network, and recommending potential solutions for existing problems. Empirical insights derived from this study provide clues to understand China’s research strengths and the science policies underlying them, along with the key research problems and solutions that Chinese researchers are focusing on now and might pursue in the future.

Ethics and privacy of artificial intelligence: Understandings from bibliometrics

“…Topic extraction is also of significant interest to the bibliometric community, in which citation statistics and textual elements are heavily used [8,32]. These extracted topics represented by either a sub-collection of documents or a set of terms hold recognized capabilities in knowledge interpretation and exploration, e.g., profiling research disciplines and technological areas [7,33], identifying latent relationships [10,15,34], and predicting potential future changes in either collaborative patterns or research interests [35][36][37]. However, regarding the characteristics of bibliometric documents and the urgent need to interpret topics in depth, we anticipate two emergent directions of topic extraction: 1) since research topics are constantly changing (e.g., cross-/inter-/multi-disciplinary interactions) rather than being stable [15], extracting topics and discovering their relationships from a dynamic perspective could be practically significant for not only the bibliometric community but also business and management studies; and 2) hierarchy is an innate structure of knowledge composition, as well as topics.…”

“…#1 AI techniques and potential ethical issues: Figure 7 reveals the key AI techniques that may raise ethical concerns, such as machine learning (including deep learning, computer vision, neural networks, natural language processing, etc. ), ontologies, communication technologies, and neuroscience 10 . Machine learning, one of the key areas in AI, shares close connections with almost all AI techniques, and thus attracts the most attention in this THT and are connected with all ethical issues, such as fairness, discrimination, liability, frauds, and criminals 11 .…”

“…Bibliometrics, which is a set of approaches for analyzing scientific documents (e.g., research articles, patents, and academic proposals), has been widely used as a tool for science, technology and innovation studies [7], such as identifying technological topics [8], discovering latent relationships [9], and predicting potential future trends [10]. Recently, AI has received recognition in bibliometrics as an emerging topic for empirical investigation [11,12].…”

Understanding Chinese science: New scientometric perspectives

“…Undoubtedly, each paper has its merits as well as room for improvement. Some readers may find the methods-for instance those introduced by Zhang, Wu, et al (2021) and Liu et al (2021)-valuable, while others may find some arguments a bit strong without excluding alternative explanations (Liu et al, 2021). In a similar vein, some readers may appreciate the combined use of multiple data sources for profiling international collaboration (Zhu et al, 2021) and for funding agency name cleaning and consolidation (Liu et al, 2021), while others may question some of the methodological choices.…”

Editorial: Advanced Analytics and Decision Making for Research Policy and Strategic Management

“…Further, the rapid development of information technologies, particularly, artificial intelligence and data science techniques, brings evolutionary changes to traditional bibliometrics, as well as its broad applications. For example, large-scale data analytics facilitates citation networks and textual segments to identify emerging topics and discover latent connections among multiple bibliometric sources (Takano and Kajikawa, 2019;Mejia and Kajikawa, 2020;Mejia and Kajikawa, 2021), and network analytics leverage understandings on the topological structure of bibliometric networks to enable new angles for evaluating science, technology, and innovation (Zhang et al, 2021a(Zhang et al, , 2021b.This topic aims to explore the ways of fully facilitating the power of advanced analytics to enhance its capability in decision support for research policy and strategic management in scalable, uncertain and complicated environments in the real world. Targeting to broad issues in science, technology and innovation (ST&I) studies, this topic emphasizes the development and applications of advanced analytic approaches that provide transparent, explainable, and evaluable evidence for decision making.…”