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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Using network science and text analytics to produce surveys in a scientific topic

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Autor(es):
Silva, Filipi N. [1] ; Amancio, Diego R. [2] ; Bardosova, Maria [3] ; Costa, Luciano da F. [1] ; Oliveira, Jr., Osvaldo N. [1]
Número total de Autores: 5
Afiliação do(s) autor(es):
[1] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Carlos, SP - Brazil
[2] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[3] Tyndall Natl Inst, Cork - Ireland
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: Journal of Informetrics; v. 10, n. 2, p. 487-502, MAY 2016.
Citações Web of Science: 21
Resumo

The use of science to understand its own structure is becoming popular, but understanding the organization of knowledge areas is still limited because some patterns are only discoverable with proper computational treatment of large-scale datasets. In this paper, we introduce a network-based methodology combined with text analytics to construct the taxonomy of science fields. The methodology is illustrated with application to two topics: complex networks (CN) and photonic crystals (PC). We built citation networks using data from the Web of Science and used a community detection algorithm for partitioning to obtain science maps of the fields considered. We also created an importance index for text analytics in order to obtain keywords that define the communities. A dendrogram of the relatedness among the subtopics was also obtained. Among the interesting patterns that emerged from the analysis, we highlight the identification of two well-defined communities in PC area, which is consistent with the known existence of two distinct communities of researchers in the area: telecommunication engineers and physicists. With the methodology, it was also possible to assess the interdisciplinary and time evolution of subtopics defined by the keywords. The automatic tools described here are potentially useful not only to provide an overview of scientific areas but also to assist scientists in performing systematic research on a specific topic. (C) 2016 Elsevier Ltd. All rights reserved. (AU)

Processo FAPESP: 14/20830-0 - Modelagem e reconhecimento de padrões em textos com redes complexas
Beneficiário:Diego Raphael Amancio
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias
Beneficiário:Roberto Marcondes Cesar Junior
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 15/08003-4 - Abordagem de redes complexas em e-Science e dados dinâmicos
Beneficiário:Filipi Nascimento Silva
Linha de fomento: Bolsas no Brasil - Pós-Doutorado