資料搜尋系統視覺化與多維度分析之設計：以資訊工程研究論文檢索系統為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：470

、訪客IP：18.224.69.137

姓名

潘立人(Li-ren Pan) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

資料搜尋系統視覺化與多維度分析之設計：以資訊工程研究論文檢索系統為例
(Visualization and Online Analytical Processing for IR Systems: A Case Study on CS Research Papers Search)

相關論文

★ 行程邀約郵件的辨識與不規則時間擷取之研究	★ NCUFree校園無線網路平台設計及應用服務開發
★ 網際網路半結構性資料擷取系統之設計與實作	★ 非簡單瀏覽路徑之探勘與應用
★ 遞增資料關聯式規則探勘之改進	★ 應用卡方獨立性檢定於關連式分類問題
★ 中文資料擷取系統之設計與研究	★ 非數值型資料視覺化與兼具主客觀的分群
★ 關聯性字組在文件摘要上的探討	★ 淨化網頁：網頁區塊化以及資料區域擷取
★ 問題答覆系統使用語句分類排序方式之設計與研究	★ 時序資料庫中緊密頻繁連續事件型樣之有效探勘
★ 星狀座標之軸排列於群聚視覺化之應用	★ 由瀏覽歷程自動產生網頁抓取程式之研究
★ 動態網頁之樣版與資料分析研究	★ 同性質網頁資料整合之自動化研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

現今過量資訊常讓使用者無法適時獲得需要資訊。以研究論文而言，當研究新鮮人欲踏入某一新領域時，須花費許多時間並過濾大量研究資訊後，方能找到有興趣的研究方向。現行搜尋系統係透過內控的Rank機制進行運算，呈現方式亦均為條列式（View by list）的資訊展現，大量資訊閱讀常花費許多時間。為使搜尋結果能讓使用者更易於接受，本論文針對電腦科學論文開發一套搜尋系統，整合現行熱門且具權威性的學術網站資訊，配合資訊擷取技術，及視覺化與OLAP互動分析概念，提出一套新穎的論文搜尋系統。由於論文研究方法、技術及應用領域、環境的異同之處，常常是該篇論文有所貢獻或獨到見解之所在，為讓使用者能掌握研究論文的精髓與精要，本篇論文針對論文標題，透過機器學習方式建立學習模組進行該篇論文可能的運用層面/環境（Application/Environment, AE）及使用方法/技術（Method/Technique, MT）的擷取，再藉由與使用者互動方式探尋其有興趣或欲研讀的論文集。

摘要(英)

Information overload is an increasing problem which often causes inefficiency. Take junior graduates as an example, searching good research papers can take a lot of time. Existing retrieval systems often display search results based on some internal ranking score. In this paper, we propose the integration of information extraction with OLAP (online analytical processing) and visualization technique to achieve interactive search experience. We define two extraction targets: application environment (AE) and involved method/technique (MT) as the basic elements that define the research problem and the contribution of a paper. Both manually constructed rules and machine learning based approaches are applied to compared the performances. The experimental results on 500 titles show that AE has an F-measure of 0.73 while MT has an F-measure of 0.6. With the extracted application environment and involved method/technology, users can then use visualization and OLAP tool to get a better view of the search results.

關鍵字(中)

★ 搜尋引擎
★ 資訊視覺化
★ 資訊檢索
★ 機器學習

關鍵字(英)

★ machine learning
★ Information retrieval
★ information visualization
★ search engine

論文目次

1. 前言.......................................................................................................... 1
1.1 研究背景........................................................................................................... 1
1.2 研究動機與目標............................................................................................... 2
1.3 研究架構........................................................................................................... 2
2. 相關研究.................................................................................................. 3
2.1 搜尋引擎........................................................................................................... 3
2.2 資訊檢索........................................................................................................... 3
2.3 視覺化............................................................................................................... 4
3. 研究架構.................................................................................................. 8
3.1 系統架構........................................................................................................... 8
3.2 網頁原始資料取得........................................................................................... 9
3.3 搜尋結果擷取................................................................................................. 10
3.4 研究論文多維度分析.......................................................................................11
3.4.1 機器學習模組...................................................................................... 12
3.4.2 AE/MT 聚合.......................................................................................... 15
3.4.3 資訊視覺化.......................................................................................... 16
3.5 系統介面設計................................................................................................. 17
3.5.1 研究論文多維度分析介面設計.......................................................... 18
4.研究結果.................................................................................................. 22
4.1 實驗環境說明................................................................................................. 22
4.2 實驗設計......................................................................................................... 22
4.3 人工規則模組................................................................................................. 25
4.3.1 論文標題分析...................................................................................... 25
4.3.2 實驗設計.............................................................................................. 27
4.4 實驗結果與討論............................................................................................. 28
4.4.1 Label Match 結果.................................................................................. 28
4.4.2 Unit Term Match 結果........................................................................... 29
5. 結論與未來發展.................................................................................... 31
6. 參考文獻................................................................................................ 33

參考文獻

[1]A. Jesse; H. Nissan, “We know the web is big”, July 2008.
[2]M. J. Pazzani. A Framework for Collaborative, Content-Based and Demographic Filtering, Artificial Intelligence Review, 1999
[3]N. Gershon, S. G. Eick, and, S. Card, “Information Visualization”, ACM Interactions, pp. 9-15, April 1998.
[4]P. Melville, R.J. Mooney, R. Nagarajan. “Content-Boosted Collaborative Filtering for Improved Recommendations”, Proceedings of the Eighteenth National Conference, 2002.
[5]R. Ghani and A. Fano. “Building recommender systems using a knowledge base of product semantics”. In Proceedings of the Workshop on Recommendation and Personalization in E-Commerce, May 2002.
[6]R. Burke, “Hybrid recommender systems: Survey and experiments”, User Modeling and User-Adapted Interaction, 12（4）:331–370, 2002.
[7]R. Burke. “Knowledge-based recommender systems”, Encyclopedia of Library & Information Systems", 2000
[8]R. Beale, R. J. McNab, I. H. Witten, “Visualizing Sequences of Queries: A New Tool for Information Retrieval”, In Proceedings of 1997 IEEE Conference on information Visualization, pages 57-62, August 1997.
[9]R. Baeza-Yates, B. Ribeiro-Neto, “Modern information retrieval”, ACM Press, New York, 1999.
[10]S. E. Robertson, K. S. Jones, “Relevance weighting of search terms”, Journal of the American Society for Information Science, pp. 129–146, May-June 1976.
[11]S. Johnson, The Ghost Map: The Story of London's Most Terrifying Epidemic--and How It Changed Science, Cities, and the Modern World, Riverhead Trade, October 2, 2007.
[12]V. Bush, “As We May Think”, The Atlantic Monthly , July 1945.
[13]Y. B. Shrinivasan, J. J. Wijk, ”Supporting the analytical reasoning process in information visualization”, Conference on Human Factors in Computing Systems, pp.1237-1246, ACM, 2008.

指導教授

張嘉惠(Chia-Hui Chang)

審核日期

2010-7-27

推文