Automating Research Problem Framing and Exploration through Knowledge Extraction from Bibliometric Data

Abstract

The steep rate at which the number of research outputs has been growing (e.g., books, journal articles, conference proceedings, patents, and other work in digital format) produces not only many intriguing opportunities but also significant challenges. Efficiently managing the research outputs in various very large digital databases has become much more difficult and error-prone than before. It is hard to precisely track all published documents in a way that is usable by humans to frame and explore new research problems. Methodologies and software tools are necessary to automate the time and resource-consuming activities in research. This chapter overviews the existing work and envisioned opportunities to automate the analysis of the research outputs available in digital databases to maximize the research quality and impact. A novel system architecture is also suggested to support research problem framing and exploration. The architecture includes smart recommender modules that also address other research activities, like researcher and institution assessment, bibliography recommendation, and research team formation.