期刊
INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
卷 21, 期 1, 页码 61-73出版社
SPRINGER
DOI: 10.1007/s00799-018-0260-z
关键词
Research expertise categorization; Classification schemes; Supervised classification
资金
- project MASWeb [FAPEMIG/PRONEX APQ-01400-14]
- CAPES
- CNPq
- FAPEMIG
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.
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