視覺化自動分群生物醫學文獻檢索系統建置之研究

隨著網際網路的發展，使用線上資料庫搜尋學術文獻，可解決以往查閱紙本文獻時，地域以及資料完整度的限制，因此逐漸成為主流的查閱文獻方式。由美國國家醫學圖書館國家生物技術資訊中心（National Center for Biotechnology Information, 簡稱NCBI, NLM）所建立的PubMed，蒐集了超過兩千萬篇生醫領域的文獻摘要，為目前該領域檢索文獻時，常被使用的線上資料庫。然而要從其大量的資料中，快速找到符合需求且精確的文獻，仍是一大挑戰。本研究將利用資料分群、標籤雲、關聯視覺化等技術，建構一個整合性的查詢系統。使用者可以利用此系統查詢PubMed上的文獻，並藉由關聯視覺化、標籤雲等功能得到與搜尋語彙高度相關與高頻率出現之字詞，以進行後續其他相關文獻之搜尋；或可選擇資料分類以限縮搜尋結果，以達成搜尋精確化之目的。我們期望以此系統提昇研究人員搜尋文獻的效率，進而縮短研究整體花費之時程。

關鍵字

PubMed ；使用者搜尋介面；標籤雲；資料分類；關聯視覺化

並列摘要

Since the Internet grows, finding literatures via online databases has become a mainstream that it overcomes the limitations generated from print literatures. PubMed is a popular database built by NCBI (National Center for Biotechnology Information, NLM) that collected over twenty millions abstracts of biomedical literatures. However, it’s challenging to find literatures that meet our need fast and accurately since there is a lot of data. In this research, we will build an integrated query system using techniques of Data Categorization, Tag Cloud and Relation Visualization. Users can search literatures of PubMed via the system, get strongly associated keywords and keywords that often appeal to help doing further research. Users can also select different data categories to refine search result. We wish to help users find literatures more efficiently via the system and shorten the time spending through entire research.

並列關鍵字

PubMed ； Search User Interface ； Tag cloud ； Data Categorization ； Relation Visualization

參考文獻

Hearst, M. A. (2006). Clustering versus faceted categories for information exploration. Commun. ACM, 49(4), 59-61.

Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE- (pp. 282–289).

Shneiderman, B. (1996). The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Proceedings of the 1996 IEEE Symposium on Visual Languages (p. 336--).

Sinclair, J., & Cardew-Hall, M. (2008). The folksonomy tag cloud: when is it useful? J. Inf. Sci., 34(1), 15-29.

amazon Available at: http://www.amazon.com

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視覺化自動分群生物醫學文獻檢索系統建置之研究

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