政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/35251
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113392/144379 (79%)
Visitors : 51202127      Online Users : 902
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/35251


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/35251


    Title: 文獻關聯之視覺化瀏覽平台建構研究
    Building a Visualization Platform for Browsing Academic Paper Relationships
    Authors: 趙逢毅
    Chao,August
    Contributors: 楊亨利
    Yang,Heng Li
    趙逢毅
    Chao,August
    Keywords: 引文網路分析
    社會網路分析
    知識本體
    視覺化資料礦探採
    Citation Network Analysis
    Social Network Analysis
    Ontology
    Visual Data Mining
    Date: 2007
    Issue Date: 2009-09-18 14:33:06 (UTC+8)
    Abstract: 每一項學術研究進行，其理論基礎都必需要建立於過去已完成的研究之上，因此文獻尋找與探討是進行研究過程非常重要的一個步驟。在數位時代與網際網路的加乘效益之下，改變了過去研究者必需為參考文獻東奔西跑的文獻資料尋找方式，但是卻會造成研究者被許多數位文獻淹沒。借用自網頁分析技術而設計的Google學術搜尋網路工具，能透過已經計算好的文獻權重PaperRank排序使用者所尋找的文獻集合，讓使用者能在數位文獻之中依單篇文獻被引用次數為原則而理出頭緒，但其順序式的排列仍然不能夠揭露出搜尋到的文獻集合裡彼此之間的關聯，其中包括了文獻所使用的關鍵字、作者與參考文獻。為了處理了解文獻中多維度的複雜資料關聯，最好的方式還是依賴人類的視覺化資訊處理能力，特別是當資料量大並且需要在短時間內決策時。
    此外使用在文獻分析研究中，學者們使用共同引用(co-citation)、共同作者(co-work)、共同作者引用(co-author)等分析方式，配合延伸自社會網路分析理論中的社會密度(social distance)、關聯層級(social degree)、群(clique)等參數概念，試將複雜的文獻資料有脈絡地按排供參考。僅管此是工作難以機械化且消耗時間的(Börner, Chen , Boyack, 2003)，但是卻能將某一特定領域的發展直覺地呈現出來，如此若能將這些分析方式配合視覺化的呈現，則研究學者便能更進一步了進行大量文獻資料視覺化的分析、探索。
    本研究試提出一個新的協助文獻探索平台系統架構，將傳統的文字搜尋轉變為視覺化的資料探索。使用者能透過三種不同的層級的資料：知識本體與關鍵字層、引文網路層及人員網路層，並與呈現的資料互動進一步了解資料間的關聯方式。最後實作視覺化雛型平台，並使用在國家圖書館所提供的博、碩士論文網所提供的論文資料，提供給研究人員探索特定知識領域中新研究方向的探索工具，並能協助研究者能在尚未完瞭解的專業領域之前，能快速地瞭解在該其領域重要文獻的導引平台。
    Paper survey is the most important task for building earnest theories, while researchers conducting academic researches. One must touches the fundamental detail of each theory and track down the develop-path of what achievement have been established by previous researches. Benefit from synergy of information age and document digitalized, it not only reduces the cost of finding reference documents, but also makes researchers suffer from information overwhelming after click single “search it” bottom. Stand in for traditional paper web search methods, new academic paper search technology borrowing from the idea of web search engine calculates the importance of each paper by cited number, and recommends users the most important papers by serial listing. However, serial listing does never spell the relationships of suggesting papers out, but only those results match some specific criteria. Those relationships of papers can be classified into 3 different types: the relations of keywords and references that author used and social relationship of authors like co-author and author co-citation which have been developed to explain the complex citation network structures. Those multi-dimensional relationships are extremely abundant and complex, so there is no better way to deal with but depending on visual data processing within human nature.
    In this paper, we try to propose a new platform to transform paper search in serial listing, into a visualized explore platform by demonstrating 3 different types of relationship: ontology-keywords, papers-references and personnel-references. End users can fallow the relationships between each difference nodes to explore considerable references, as well as change into different view and interact with existing information by using interactive mechanizes. In order to bring this idea to practical application usage, we build a proto-type platform to show our idea by using data from ETDS (electronic theses and dissertations system) of Ministry of education. We hope sincerely by using this proto-type platform, users can catch the major ideas of specific knowledge domain and researchers can explore acceptable references and even conduct new search topic.
    Reference: 1. 陳俊彰（2001），從網頁中發掘教師知識分佈，國立中山大學資訊管理學系研究所碩士論文。
    2. 陳榮昌、蔡旺典（2006），以知識本體論來輔助個人化排序，朝陽科技大學資訊管理所。
    3. 陳銘翔（2006），複雜網路有效視覺化-以引文網路為例，國立台北大學資訊管理研究所碩士論文。
    4. 曾信誠（2004），以本體論為基礎之使用者喜好萃取、隱私權控管與側解建構，國立東華大學資訊工程學系碩士論文。
    5. 王奕涵（2006），正規化概念分析的資訊管理領域理論之知識本體建構，國立雲林科技大學資訊管理碩士論文。
    6. 丁一賢，陳牧言（2005），資料探勘 Data Mining，(初版) ，滄海書局。
    7. 楊亨利，趙逢毅（2007），協助搜尋文獻的平台之芻議，十三屆海峽兩岸資訊管理發展與策略學術研討會，八月，北京交通大學，中國北京。
    8. 楊亨利，趙逢毅（2007），建構在全國博、碩士論文資訊網上的視覺化文獻互動關聯式瀏覽平台架構，第六屆管理新思維研討會，十一月，台灣科技大學，台灣台北。
    9. 梁定澎（2003），管理一及管理二學門國際學術期刊分級及排序專案計畫，行政院國家科學委員會專題研究計畫。
    10. Amber，“Google Scholar建立符合研究人員直覺的排名新準則”， 產業策略評析，http://cdnet.stpi.org.tw/techroom/analysis/pat_B033.htm [access 2008/7/6]
    11. Adomavicius G. and Tuzhilin A. (2005), “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions”, Vol. 17, No. 6, pp.734-749.
    12. Börner K., Chen C., and Boyack K. W. (2003), “Visualizing Knowledge Domains,” Annual Review of Information Science and Technology, Vol. 37, No. 1, pp.179-255.
    13. Berry M. J. A. and Linoff G. S. (2001)，資料採礦─顧客關係管理暨電子行銷應用─，(初版) ，彭文正譯，維科出版社。
    14. Chen C. (2006), “CiteSpace II: Detecting and Visualizing Emerging Trends and Transient Patterns in Scientific Literature,” Journal of the American Society for Information Science and Technology, Vol. 57, No. 3, 2006, pp.359-377.
    15. Chen C. and Paul R. J. (2001), “Visualizing a Knowledge Domain`s Intellectual Structure,” Computer, Vol. 34, No. 1, 2001, pp.65-71.
    16. Garfield E., Sher I. H., and Torpie R. J. (1964), “The Use of Citation Data in Writing the History of Science,” Philadelphia: Institute for Scientific Information.
    17. Gori, M. and Pucci, A. (2006), “Research Paper Recommender Systems: A Random-Walk Based Approach”, Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 778-781.
    18. Heer J., Card S. K., and Landay J. A. (2005), “Landay, Prefuse: A Toolkit for Interactive Information Visualization,” Conference on Human Factors in Computing Systems, pp.421-430.
    19. Herlocker, J. L. and Konstan, J. A. (2004), ‘Content-independent task-focused recommendation’, Internet Computing, IEEE, Vol. 5, No. 6, pp. 40-47.
    20. Hwang, S.Y. and Chuang, S.M. (2004), “Combining article content and Web usage for literature recommendation in digital libraries”, Vol. 28, No. 4, pp. 260-272.
    21. Hand D., Mannila H., and Smyth P. (2001) Principles of Data Mining, The MIT Press.
    22. Keim A. D. (2002), “Information Visualization and Visual Data Mining,” Visualization and Computer Graphics, IEEE Transactions on, Vol. 8, No. 1, pp.1-8.
    23. Lawrence S., Bollacker K. (1999), “Digital Libraries and Autonomous Citation Indexing,” Contact, Vol. 32, pp.67-71.
    24. Noy N. F., and McGuinness D. L. (2001), “Ontology Development 101: A Guide to Creating Your First Ontology, ”Technical Report SMI-2001-0880, Stanford Medical Informatics.
    25. Roiger R. J. and Geatz M. W. (2003)，資料探勘 Data Mining: A Tutorial-based Primer，(初版) ，曾新穆、彭文正譯，台灣培生教育出版。
    26. Shneiderman B. and Plaisant C. (2005), Designing the User Interface: Strategies for Effective Human-Computer Interaction, (4th edition), Addison Wesley.
    27. Scott J. (2000), Social Network Analysis: A Handbook, (2nd edition), SAGE Publications.
    28. Tan P. N., Steinbach M., and Kumar V. (2005) Introduction to Data Mining, (US edition), Addison Wesley.
    29. Uschold M., and Gruninger M. (1996), “Ontologies: Principles, Methods, and Applications,” To appear in Knowledge Engineering Review, Vol. 11, No. 2, pp.93-136.
    30. Williams G. J. and Simoff S. J. (2006) Data Mining: Theory, Methodology, Techniques, and Applications,(1st edition), Springer.
    31. http://scholar.google.com.tw/intl/zh-TW/scholar/about.html [access: 2008/7/6]
    32. http://etds.ncl.edu.tw/theabs/index.jsp, [access: 2008/7/6]
    33. http://framework.zend.com/ [access: 2008/7/18]
    Description: 碩士
    國立政治大學
    資訊管理研究所
    95356019
    96
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0095356019
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

    Files in This Item:

    File Description SizeFormat
    601901.pdf49KbAdobe PDF2639View/Open
    601902.pdf230KbAdobe PDF2674View/Open
    601903.pdf183KbAdobe PDF2737View/Open
    601905.pdf328KbAdobe PDF2767View/Open
    601906.pdf363KbAdobe PDF2820View/Open
    601907.pdf987KbAdobe PDF2732View/Open
    601908.pdf2628KbAdobe PDF2890View/Open
    601909.pdf210KbAdobe PDF2687View/Open
    601910.pdf169KbAdobe PDF21003View/Open
    601911.pdf133KbAdobe PDF2719View/Open
    60194.pdf156KbAdobe PDF2682View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback