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  • P-ISSN1013-0799
  • E-ISSN2586-2073
  • KCI

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Vol.32 No.2

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Detection of Hidden Knowledge Using a Citation-Based Approach Based on Swanson's ABC Model

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2015, v.32 no.2, pp.87-103
https://doi.org/10.3743/KOSIM.2015.32.2.087


& (2015). Detection of Hidden Knowledge Using a Citation-Based Approach Based on Swanson's ABC Model. Journal of the Korean Society for Information Management, 32(2), 87-103, https://doi.org/10.3743/KOSIM.2015.32.2.087

Abstract

It is useful to find something valuable for researching through literature based discovery. Swanson’s ABC model, known as literature based discovery, suggests the relationship between entities undiscovered yet. This study tries to find the valid relationship between entities by referring to citation which connects articles on similar topic. We collect citation from references in articles, and extract important concepts in titles and abstracts through text mining techniques. We reproduce the relationship between fish oil and Raynaud’s disease, which is known as one of Swanson’s works, and compare the results with entities identified from traditional approach.

keywords
text mining, entitymetrics, literature based discovery, ABC model, 텍스트마이닝, 개체계량학, 문헌기반 발견, ABC 모델

Reference

1.

Bastian M.. (2009). Gephi: An open source software for exploring and manipulating networks (-). In Proceedings of the 3rd International Association for the Advancement of Artificial Intelligence Conference on Weblogs and Social Media.

2.

Cameron, D.. (2013). A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications. Journal of biomedical informatics, 46(2), 238-251.

3.

Ding, Y.. (2013). Entitymetrics:Measuring the impact of entities. PloS one, 8(8), e71416-.

4.

Hristovski, D.. (2005). Using literature-based discovery to identify disease candidate genes. International Journal of Medical Informatics, 74(2), 289-298.

5.

Otte, E.. (2002). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28(6), 441-453.

6.

Pratt, W.. (2003). October. LitLinker: Capturing connections across the biomedical literature (-). In Proceedings of the 2nd International Conference on Knowledge Capture.

7.

Song, M.. (2013). Discovering implicit entity relation with the gene-citation-gene network. PloS One, 8(12), e84639-.

8.

Srinivasan, P.. (2004). Text mining: Generating hypotheses from MEDLINE. Journal of the American Society for Information Science and Technology, 55(5), 396-413.

9.

Weeber, M.. (2001). Using concepts in literaturebased discovery: Simulating Swanson's Raynaud-fish oil and migraine-magnesium discoveries. Journal of the American Society for Information Science and Technology, 52(7), 548-557.

10.

Wren, J. D.. (2004). Knowledge discovery by automated identification and ranking of implicit relationship. Bioinformatics, 20(3), 389-398.

11.

Yetisgen-Yildiz, M.. (2009). A new evaluation methodology for literature-based discovery systems. Journal of Biomedical Informatics, 42(4), 633-643.

12.

Yu, Q.. (2015). Tracing database usage:Detecting main paths in databaselink networks. Journal of Informetrics, 9(1), 1-15.

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