LitStoryTeller+: an interactive system for multi-level scientific paper visual storytelling with a supportive text mining toolbox

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

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.