期刊電子化已成為趨勢，加上網際網路的發達，人們使用網路電子期刊的數量逐日攀升。每月皆有新的電子期刊出版，使得查詢到期刊的資訊量以遠遠超過人們所能處理的能力。面對此一問題，現今已有許多大量的電子期刊資料庫提出keyword搜尋方式，以減輕使用者在搜尋期刊論文上所需花費的精力及時間。僅管如此，使用者仍須面對龐大的搜尋結果，並無較佳的呈現方式來協助使用者快速過濾相關文獻。如何在電子期刊資料庫上正確且快速地檢索與擷取使用者所需要的資訊，並完整且友善地呈現搜尋結果來改善文獻搜集上所耗費的時間，已成為現今重要的研究議題。
因現今的電子期刊資料庫皆是透過使用者所輸入的關鍵字，搜尋並擷取資料庫中所有包含此關鍵字的電子期刊，並單純以列表的方式呈現搜尋結果。若能歸納出文獻主題，並將搜尋結果以此文獻主題加以區隔，則可以「主題-文獻」的階層方式呈現搜尋結果給使用者，改善傳統列表呈現模式的問題。雖已有學者利用主題偵測(topic detection)的技術來逹到此目標，但他們的方法是以全文為基礎來應用於一般網路文件。此方法並不適用於文獻期刊，因期刊有文獻結構上的特性(如題目、關鍵字及摘要)，同時目前方法在比對上也純粹以「字」的方式進行比對，而忽略了「語意」的重要性。有鑑於此，本研究將提出以文獻結構特性和語意的方式擷取出每篇文獻的重要字集及分群運算，並抽取出每一群的主題加以呈現搜尋結果。期使用者能透過「主題 - 群」的階層呈現方式縮短文獻收集的時間，正確找到其所需之資訊。

Because of the development of Internet, electronic journals have become a trend. The quantities of people use electronic journals more than before. The amount of electronic journal articles grow faster than before, it leads that information generated over the ability that people can deal with. In order to deal with this problem, a lot of electronic periodical databases have proposed keyword search methods to lighten user's effort and time spending in searching the journal papers. However, the users still have to face the huge search results. Currently, there is no better way of representing to help users to speed up filtering relevant documents. How to provide a efficient search, i.e. present the search result in categories, have became an important research topic now.
Today’s electronic journal databases apply keywords search which user inputs interested terms and search engine find the papers which contains keywords and show the searching results by the way of tabulating. If these results can be generalized and classify by their topics, then we can show the search results to users by the topic which should be able to improve tradition display method.
Though scholars have employed topic detection method to achieve this goal in full-text documents on network. However, literatures have the structure properties, such as title, keyword, and abstract, not only full-text. Simultaneously, traditional topic detection method only uses the word frequency feature, ignores the importance of semantic. Therefore, the proposed research designs a method which is based on literature structure and semantic properties to extract important words and cluster to each literature. It can retrieve each group topics and display the searching results by these topics. Expect users can reduce literature collection time and find correctly information by topic-cluster display way.

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網站資料
TDT (Topic Detection and Tracking), http://www.nist.gov/speech/tests/tdt/
Vivisimo, http://www.vivisimo.com
Formal Concept Analysis Homepage,
http://www.upriss.org.uk/fca/fca.html#einleitung