Journal of the Korean Society for Information Management 2023 KCI Impact Factor : 1.31
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pISSN : 1013-0799 / eISSN : 2586-2073
- https://journal.kci.go.kr/kosim
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Construction of Research Fronts Using Factor Graph Model in the Biomedical Literature
- Journal of the Korean Society for Information Management
- Abbr : JKOSIM
- 2017, 34(1), pp.177~195
- DOI : 10.3743/KOSIM.2017.34.1.177
- Publisher : 한국정보관리학회
- Research Area : Interdisciplinary Studies > Library and Information Science
- Received : February 20, 2017
- Accepted : March 6, 2017
- Published : March 30, 2017
Kim, Hea-Jin 1, Min Song 1
1연세대학교
Accredited
ABSTRACT
This study attempts to infer research fronts using factor graph model based on heterogeneous features. The model suggested by this study infers research fronts having documents with the potential to be cited multiple times in the future. To this end, the documents are represented by bibliographic, network, and content features. Bibliographic features contain bibliographic information such as the number of authors, the number of institutions to which the authors belong, proceedings, the number of keywords the authors provide, funds, the number of references, the number of pages, and the journal impact factor. Network features include degree centrality, betweenness, and closeness among the document network. Content features include keywords from the title and abstract using keyphrase extraction techniques. The model learns these features of a publication and infers whether the document would be an RF using sum-product algorithm and junction tree algorithm on a factor graph. We experimentally demonstrate that when predicting RFs, the FG predicted more densely connected documents than those predicted by RFs constructed using a traditional bibliometric approach. Our results also indicate that FG-predicted documents exhibit stronger degrees of centrality and betweenness among RFs.
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This paper was written with support from the National Research Foundation of Korea.