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

많은 연구들 가운데 살펴볼 가치가 있는 대상을 찾아 제시해주는 문헌기반 발견의 접근법은 연구자들에게 매우 유용할 것이다. 문헌기반 발견 연구의 대표 이론인 Swanson의 ABC 모델은 기존에 검증되지 않은 개체들의 관계를 연구할 것을 제안해 준다. 본 연구는 Swanson의 ABC 모델에 인용 정보를 고려하여 유의한 관계에 있는 개체들을 더 효율적으로 찾아내고자 하였다. 수집 논문들의 참고문헌 목록에서 인용 정보를 확인하고 논문의 표제와 초록을 대상으로 텍스트 마이닝 기법으로 중요한 단어들을 추출하였다. Swanson의 연구들 중 어유와 레이노드 질병 및 증상의 관계를 재현하였으며 기존의 접근법으로 확인되는 개체들과 어떤 차이가 있는지 분석하였다.

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

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

참고문헌

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.

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

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

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

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

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

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

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

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.

바로가기메뉴

논문 상세

Vol.32 No.2

인용 정보를 고려한 미발견 공공 지식 추출: Swanson의 ABC 모델 재현 및 확장

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

초록

Abstract

참고문헌

정보관리학회지