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- E-ISSN2586-2073
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Construction of Research Fronts Using Factor Graph Model in the Biomedical Literature
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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.
- keywords
- Bibliographic features, content features, factor graph model, network features, probabilistic graphical model(PGM), research front, 내용 자질, 네트워크 자질, 서지 자질, 심층학습, 연구전선, 인용분석, 팩터그래프, 확률 그래프 모델
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