臺灣博碩士論文加值系統

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。

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.

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

[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.