Tag Archives: SOA Analysis

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

Uncovering the driving forces, strategic landscapes, and evolutionary mechanisms of China’s research systems is attracting rising interest around the globe. One such 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 beneath them, along with the key research problems and solutions Chinese researchers are focusing on now and might pursue in the future.

FULL-TEXT available at https://www.mitpressjournals.org/doi/pdf/10.1162/qss_a_00100

Author(s): Yi Zhang, Mengjia Wu, Zhengyin Hu, Robert Ward, Xue Zhang, Alan Porter
Organization(s): University of Technology Sydney, Chengdu Library and Information Centre (CAS), Georgia Institute of Technology
Source: Quantitative Science Studies
Year: 2020

Identification of technology development trends based on subject–action–object analysis: The case of dye-sensitized solar cells

Identification of technology development trends is essential for supporting decision makers in forecasting and identifying related innovation activities and industrial growth. Different from the traditional technology development trends based on keyword-based quantitative methods, which usually predict trends by finding key technologies without showing how to develop them, our method allows the identification of future direction and industry goal for the technology domain and shows detailed paths for achieving them. Thus, our method has constructed technology roadmapping (TRM) with seven layers (material, technology, influencing factor, component, product, goal, and future direction) on the basis of subject–action–object analysis. The detailed paths for developing this as a trend can be shown by the interaction among these TRM elements. In addition, the method also sets three indicators as a discriminating standard to find key players that can support the trend by engaging technological innovation scenarios. The case of a dye-sensitized solar cell (DSSC) is exemplified to illustrate the detailed procedure of our method. The results reveal the development trends in the field of DSSCs, the detailed paths to achieve them, and key countries that support them.


Author(s): Xuefeng Wang, Pengjun Qiu, Donghua Zhu, Liliana Mitkova, Ming Lei, and Alan L. Porter
Organization(s): Beijing Institute of Technology, University Paris-Est Marne la Vallée, and Georgia Institute of Technology
Source: Technological Forecasting and Social Change
Year: 2015