A pragmatic approach to hierarchical categorization of research expertise in the presence of scarce information

Gustavo Oliveira de Siqueira, Sérgio D. Canuto, Marcos André Gonçalves, Alberto H. F. Laender

Abstract: Throughout the history of science, different knowledge areas have collaborated to overcome major research challenges. The task of associating a researcher with such areas makes a series of tasks feasible such as the organization of digital repositories, expertise recommendation and the formation of research groups for complex problems. In this article, we propose a simple yet effective automatic classification model that is capable of categorizing research expertise according to a knowledge area classification scheme. Our proposal relies on discriminatory evidence provided by the title of academic works, which is the minimum information capable of relating a researcher to its knowledge area. Our experiments show that using supervised machine learning methods trained with manually labeled information, it is possible to produce effective classification models.