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題名 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
作者 趙逢毅
Chao,August
貢獻者 楊亨利
Yang,Heng Li
趙逢毅
Chao,August
關鍵詞 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
日期 2007
上傳時間 18-Sep-2009 14:33:06 (UTC+8)
摘要 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.
參考文獻 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]
描述 碩士
國立政治大學
資訊管理研究所
95356019
96
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0095356019
資料類型 thesis
dc.contributor.advisor 楊亨利zh_TW
dc.contributor.advisor Yang,Heng Lien_US
dc.contributor.author (Authors) 趙逢毅zh_TW
dc.contributor.author (Authors) Chao,Augusten_US
dc.creator (作者) 趙逢毅zh_TW
dc.creator (作者) Chao,Augusten_US
dc.date (日期) 2007en_US
dc.date.accessioned 18-Sep-2009 14:33:06 (UTC+8)-
dc.date.available 18-Sep-2009 14:33:06 (UTC+8)-
dc.date.issued (上傳時間) 18-Sep-2009 14:33:06 (UTC+8)-
dc.identifier (Other Identifiers) G0095356019en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/35251-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 95356019zh_TW
dc.description (描述) 96zh_TW
dc.description.abstract (摘要) 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。
zh_TW
dc.description.abstract (摘要) 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.
en_US
dc.description.tableofcontents 一、緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究資料範圍與研究限制 3
1.5 研究流程與步驟 4
1.6 本研究內容架構 5
二、文獻探討 6
2.1 知識本體 (Ontology) 6
2.2 引文網路分析 7
2.3 社會網路分析 8
2.4 資訊視覺化與互動界面 8
2.5 文獻推薦與PaperRank 9
三、系統設計 12
3.1使用者需求分析 12
3.2規劃系統架構 14
3.3關鍵字網路、引文網路、人員網路關聯計算 21
3.4網路視覺化與互動介面設計 23
四、雛型系統實作 26
4.1雛形系統實作工具與相關技術 26
4.2雛形系統實作流程 29
4.3雛型系統操作情境 46
4.4雛型系統特色 56
4.5雛型系統限制 59
4.6雛型系統貢獻 59
五、結論及未來方向 62
六、參考文獻 65
七、附錄 68
7.1論文網中論文關鍵字與引文性質 68
7.2 論文網中論文發表單位之字義討論 69

表目錄
表1 Google網頁排名與Google Scholar挑選準則對照關係 11
表2本系統中網路型態呈現表 23
表3本系統中互動功能列表 24
表4視覺化雛形平台資料提供端Web Services服務說明 39
表5各網路層中節點與連接方向說明表 40
表6網路圖層點選節點之切換互動功能表 42
表7「一般者模式」過濾之期刊名稱 43
表8 本系統與陳銘翔 (民95)之異同 63

圖目錄
圖1 本文概念性系統架構圖 15
圖2 推薦模組說明圖組 20
圖3 三層網路間切換示意圖 25
圖4 本雛形系統實際運作的 Data Mining 知識本體 30
圖5 文獻標題處理流程虛擬碼 32
圖6 知識本體與關鍵字關聯管理界面 33
圖7 尋找中文姓名流程虛擬碼 33
圖8 正規化後的ER Diagram 34
圖9 本研究之資料表規格 35
圖10 視覺化雛形平台概念圖 37
圖11雛形系統中各層級間切換流程 42
圖12在關鍵字「ontology」下不同模式比較圖 45
圖13雛型系統之順序式查詢平台 46
圖14引力設定(預設值、建議值)比較圖 51
圖15知識本體「ontology」相關關鍵字在高關聯距離下(20)的自動群眾結果 52
圖16以「本體論」關鍵字探索視覺化雛形平台 53
圖17 研究人員情境下,關鍵字網路層(全圖)、知識本體與關鍵字網路層(全圖) 54
圖 18關鍵字「序列型樣」之相關網路圖 55
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0095356019en_US
dc.subject (關鍵詞) 引文網路分析zh_TW
dc.subject (關鍵詞) 社會網路分析zh_TW
dc.subject (關鍵詞) 知識本體zh_TW
dc.subject (關鍵詞) 視覺化資料礦探採zh_TW
dc.subject (關鍵詞) Citation Network Analysisen_US
dc.subject (關鍵詞) Social Network Analysisen_US
dc.subject (關鍵詞) Ontologyen_US
dc.subject (關鍵詞) Visual Data Miningen_US
dc.title (題名) 文獻關聯之視覺化瀏覽平台建構研究zh_TW
dc.title (題名) Building a Visualization Platform for Browsing Academic Paper Relationshipsen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 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。zh_TW
dc.relation.reference (參考文獻) 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。zh_TW
dc.relation.reference (參考文獻) 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。zh_TW
dc.relation.reference (參考文獻) 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。zh_TW
dc.relation.reference (參考文獻) 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。zh_TW
dc.relation.reference (參考文獻) 6. 丁一賢,陳牧言(2005),資料探勘 Data Mining,(初版) ,滄海書局。zh_TW
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