§ 瀏覽學位論文書目資料
  
系統識別號 U0002-2306200604574700
DOI 10.6846/TKU.2006.00722
論文名稱(中文) 以本體論協同式建構與分享領域知識之研究
論文名稱(英文) Collaborated Constructing and Sharing of Domain Knowledge using Ontology
第三語言論文名稱
校院名稱 淡江大學
系所名稱(中文) 資訊管理學系碩士班
系所名稱(英文) Department of Information Management
外國學位學校名稱
外國學位學院名稱
外國學位研究所名稱
學年度 94
學期 2
出版年 95
研究生(中文) 盧冠廷
研究生(英文) Kuan-Ting Lu
學號 693521154
學位類別 碩士
語言別 繁體中文
第二語言別
口試日期 2006-05-20
論文頁數 44頁
口試委員 指導教授 - 劉艾華(liou@mail.im.tku.edu.tw)
委員 - 陳宗天
委員 - 連志成
委員 - 周清江
關鍵字(中) 本體論
領域知識
知識管理
協同編輯
知識分享
關鍵字(英) Ontology
Domain Knowledge
Knowledge Management
Collaborative Editing
Knowledge Sharing
第三語言關鍵字
學科別分類
中文摘要
隨著資訊科技發展快速,透過網路搜尋特定領域的知識已非常方便且豐富,這樣的結果乃是拜科技的便利與人員投入所賜,不過網路分享的資料過於泛濫與格式的不同,導致搜尋到的結果繁雜,不易整理,使研究者需耗費更多的時間來對搜尋到的資料再分析、擷取為資訊。但若可以在搜尋資訊的同時,能透過詳細分類和描述來定義資訊,則能幫助於領域知識上的釐清並加強研究者對整個領域的掌握,並便於爾後領域知識在網路環境上的分享與建構、維護。為了讓研究者在研讀文獻的過程中,可以掌握文獻的屬性、類別以及與文獻重要內容之間的關聯,本研究透過建立領域知識本體論,把理解領域文獻後的知識依類別、屬性,利用協同合作方式慢慢的建構一個豐富的領域知識庫。研究者可以透過知識庫來查詢領域的相關知識,並將查詢結果輔以圖形表示來協助研究者理解領域知識,讓研究者在理解領域知識的過程中可以清楚掌握領域重要物件之間的關聯,輕易的建構出相關領域的知識概念圖。
英文摘要
Since the information technology has been developed so fast, searching the related knowledge or information in specific domain is much easier than before. However, the amount of returning information usually too large and the relations between the handling objects are usually complicated. The users can only processing the collected objects, or documents, based on their basic properties such as document name or publishing years. It is difficult for a user to manage all the deep knowledge involved based on the information mentioned or discussed within the document. However, in order to master the overall concepts of the particular domain, it is necessary for the user to be able to actually study the article and understand the real contents of it and then, some how, to record the structures and relations of the knowledge which he has mastered after reading and understand the contents elaborated in the documents. If we can classify and describe the related information easily, it would help the researcher to grasp the whole picture of the domain and enhance the quality of his work.
In this research we have shown how a researcher can improve the effectiveness of his study by easily build-up the complete domain ontology and specify the concept and the relations between the research articles in a structured way. The structure of the research domain is following a specific ontology which is determined before the experiment. The users are allowed to present the relations between the ontology objects in the articles by calculating the weightings and visualizing the arranged data and show the complete domain framework. The visualization is done by drawing relational diagrams based on the user’s query. This permits the user to master the overall concepts of the interested domain. The total framework is implemented on the web for easy accessibility. This approach of domain knowledge analysis provides more precise information about document relationships.
第三語言摘要
論文目次
目錄
第一章 緒論	1
第一節 研究背景與動機	1
第二節 研究目的	2
第三節 論文架構	4
第二章 文獻探討	6
第一節 本體論(Ontology)	6
1. 本體論的定義	6
2. 本體論的要素	8
第二節 知識管理(Knowledge Management)	8
1. 知識管理的定義	8
2. 知識管理的目的	9
第三節 協同編輯(Collaborative Editing)	10
1. 協同編輯的定義	10
2. 協同編輯的實例	11
第三章 研究架構	15
第一節 領域知識本體論的建立	15
1. 建立領域知識類別	16
2. 建立類別屬性及關聯	19
第二節 系統架構	23
1. 領域知識庫	24
2. 系統功能	24
3. 權重系統	26
4. 領域知識概念圖的呈現	28
第四章 領域知識管理系統	30
第一節 系統使用說明	30
1. 建構領域知識	30
2. 查詢領域知識	31
3. 呈現領域知識	34
4. 維護領域知識	36
第二節 系統使用效益	37
第五章 結論與未來研究方向	39
參考文獻	41
 
圖目錄
圖1 研究流程圖	5
圖2 領域知識本體論架構	18
圖3 領域知識本體論關聯圖	22
圖4 系統架構圖	23
圖5 概念圖呈現	29
圖6 知識擷取介面	30
圖7 類別之實體列表	31
圖8 知識查詢介面	32
圖9 知識查詢條件	32
圖10 領域知識查詢結果	33
圖11 階層表示查詢結果	34
圖12 圖形化表示領域知識	35
圖13 維護領域知識	36
圖14 修改領域知識	37
 
表目錄
表1領域知識類別定義	16
表2 Paper類別屬性	19
表3 Author類別屬性	20
表4 Organization類別屬性	21
表5 Published類別屬性	21
表6 Knowledge類別屬性	21
表7領域知識管理系統與CiteSeer比較	38
參考文獻
1.	Benjamins, V., D. Fensel, and A. Perez, “Knowledge Management through Ontologies,” In Proceedings of Practical Aspects of Knowledge Management, May. 1998, pp. 1-12.
2.	Brandes U., M. Eiglsperger, I. Herman, M. Himsolt, and M. S. Marshall, “GraphML Progress Report: Structural Layer Proposal,” Proc. 9th Intl. Symp. Graph Drawing (GD '01), LNCS 2265, pp. 501-512.
3.	Chandrasekaran, B., R. Josephson, and R. Benjamins, “What Are Ontologies, and Why Do We Need Them?” IEEE Intelligent Systems, Jan./Feb. 1999, pp. 20-26.
4.	Churchman C.W., “The Design of Inquiring Systems,” Basic Books, New York, 1971。 
5.	CiteSeer.IST, http://citeseer.ist.psu.edu/
6.	Davenport, T. H., “Some Principles of Knowledge Management,” Feb. 1998.
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8.	Dillenbourg, P., M. Baker, A. Blaye, and C. O'Malley, “The Evolution of Research on Collaborative Learning,” In E. Spada & P. Reiman (Eds). Learning in Humans and Machine: Towards an interdisciplinary learning science, Oxford: Elsevier, 1996, pp. 189-211.
9.	Fikes, R., and G. Hendrix, “A Network-Based Knowledge Representation and ItsNatural Deduction System,” IJCAI 1977, 1997, pp. 235-246.
10.	Frappaolo, C., “Defining knowledge management: Four basic functions,” Computerworld, Vol. 32, No. 8:80. 1998.
11.	Gilbert, M., G. H. Martyn, “Understanding the Process of Knowledge Transfer to Achieve Successful Technological Innovation,” Technovation, Vol.16, No.6, 1996, pp.301-312.
12.	Gomez-Perez, A., “Ontological Engineering: A State of The Art,” Expert Update, Autumn 1999, Vol. 2, No. 3, pp 33-43.
13.	Gruber, T. R., “A Translation Approach to Portable Ontology Specification,” Knowledge Acquisition, Vol. 5, 1993, pp. 199-220.
14.	Gruber, T. R., “Towards Principles for the Design of Ontologies Used forKnowledge Sharing,” International Journal of Human-Computer Studies, Vol. 43, No. 5-6, 1995, pp. 907-928.
15.	Jim G., “Internet encyclopaedias go head to head: Jimmy Wales' Wikipedia comes close to Britannica in terms of the accuracy of its science entries, a Nature investigation finds.” http://npg.nature.com/news/2005/051212/full/438900a.html, Dec. 2005.
16.	JUNG(Java Universal Network/Graph Framework), http://jung.sourceforge.net/
17.	Koivunen, M. R., E. Prud’hommeaux and R. Swick, “Annotea Shared Bookmarks,” KCAP03 workshop, Oct. 2003.
18.	Lehmann, F., (ed) “Semantic Networks in Artificial Intelligence,” Elsevier ScienceInc., New York, USA, 1992.
19.	Marja, R. K., S. Ralph and P. Eric, “Annotea Shared Bookmarks, ” MIT Laboratory for Computer Science
20.	Minsky, M., “A framework for representing knowledge,” In Winston, P.J., (ed) The psychology of computer visions, McGraw-Hill, New York, 1975.
21.	Noy, N. F., M. Sintek, S. Decker, M. Crubezy, R. W. Fergerson, and M. A. Musen,  “Creating Semantic Web contents with Protege-2000,” IEEE IntelligentSystems, Vol. 16, No. 2, 2001, pp. 60–71.
22.	Noy, N. F., McGuinness, D. L., “Ontology Development 101: A Guide to Creating Your First Ontology,” Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, Mar. 2001.
23.	OWL Web Ontology Language Overview, http://www.w3.org/TR/owl-features/
24.	RDF Primer, http://www.w3.org/TR/rdf-primer/
25.	Reimer, U. et al, “Ontology-based Knowledge Management at Work: The Swiss Life Case Studies,” In J. Davies (Ed.), Towards the Semantic Web: Ontology-driven Knowledge Management, Wiley, Nov. 2002.
26.	Salton, G., “Automatic Indexing Using Bibliographic Citations,” Journal of Documentation, Vol. 27, 1971, pp. 98-110.
27.	Salton, G., C. Buckley, “Term-weighting Approaches in Automatic Text Retrieval,” Information Processing and management, Vol. 24, No. 5, 1988, pp. 513-523.
28.	Scharmer, C. O., “Self-transcending knowledge: Organization around Emerging Realities,” Managing Industrial Knowledge, In Nonaka, I. (Ed), London:SAGE, 2001.
29.	Stewart, T. A., “Intellectual Capital: The New Wealth of Organization,” Doubleday Press, 1997.
30.	Sure, Y., R. Studer, “A Methodology for Ontology-based Knowledge Management,” In J. Davies (Ed.), Towards the Semantic Web: Ontology-driven Knowledge Management, Wiley, Nov 2002.
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33.	Wikipedia, http://www.wikipedia.org
34.	Yianilos, P., “The LikeIT Intelligent String Comparsion Facility,” NEC Institute Tech Paper, 97-093, 1997.
35.	周雋偉,「經由管理研究架構掌握領域知識之研究」,智慧型知識經濟暨第二屆演化式計算應用研討論文集,2004,真理大學。
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