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International Journal on Digital Libraries

Erschienen in:

16.11.2018

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

verfasst von: Gustavo Oliveira de Siqueira, Sérgio Canuto, Marcos André Gonçalves, Alberto H. F. Laender

Erschienen in: International Journal on Digital Libraries | Ausgabe 1/2020

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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.

Vorheriger Artikel Assessing plausibility of scientific claims to support high-quality content in digital collections

Nächster Artikel Characterising online museum users: a study of the National Museums Liverpool museum website

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In this article we use the terms classification and categorization interchangeably.

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Literal translation from the original title in Portuguese: A UNESCO e o Mundo da Cultura.

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Titel: A pragmatic approach to hierarchical categorization of research expertise in the presence of scarce information
verfasst von: Gustavo Oliveira de Siqueira
Sérgio Canuto
Marcos André Gonçalves
Alberto H. F. Laender
Publikationsdatum: 16.11.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal on Digital Libraries / Ausgabe 1/2020
Print ISSN: 1432-5012
Elektronische ISSN: 1432-1300
DOI: https://doi.org/10.1007/s00799-018-0260-z

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