Detecting and Tracing Emerging Research Trends Real-Timely Using Web Data
Wang Xianwe · Science of Science and Management of S.& T · 2014
Former studies on research trends are based on published articles. Those results inevitably turn out to reveal past trends due to the publication time lag. This study, however, provides a new idea to investigate research trends. As is known to all, when scientists are considering some research topics or conducting advanced research, they tend to search and download related publications in scientific databases. Accordingly, the downloaded publications, if explored properly, would largely reflect the subjects scientists are working on. So we propose a new method. Based on the timely usage data of the web-based tools, we intend to track the research trends, delve into the hot topics, and explore the emerging research fronts in certain scientific fields. We design a trends tracing DIKW model, which is composed of Data, Information, Knowledge, and Wisdom. Specifically, we probe into neurocomputing, and establish a knowledge mining framework.