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系統識別號 U0002-0207200715010200
中文論文名稱 採用本體論及推論於資料語意偵錯之研究
英文論文名稱 Detecting Semantic Data Errors by Adopting Ontology and Reasoning
校院名稱 淡江大學
系所名稱(中) 資訊管理學系碩士班
系所名稱(英) Department of Information Management
學年度 95
學期 2
出版年 96
研究生中文姓名 林英潔
研究生英文姓名 Ying-Jie Lin
學號 694520064
學位類別 碩士
語文別 中文
口試日期 2007-06-09
論文頁數 49頁
口試委員 指導教授-劉艾華
委員-梁恩輝
委員-紀宗衡
委員-陳振東
中文關鍵字 語意網路  本體論  Jess  自動推論 
英文關鍵字 Semantic Web  Ontology  Jess  Automated Reasoning 
學科別分類 學科別社會科學管理學
學科別社會科學資訊科學
中文摘要 隨著網際網路的快速發展,多樣化的網站資料也伴隨時間與空間有所變動,如何確保資料的合理性、正確性及完整性更是一大重要課題。目前大多數網站,其資料皆是由人工方式進行輸入,然而這樣的輸入方式往往會導致輸入錯誤資料的情況發生,而產生嚴重的虧損。雖然使用程式語言對於網站資料在語法上的正確性維護並不困難,然而要做到判斷網站內容在語意上是否合理化,卻是相當地複雜,相對地也提高維護網站資訊所需花費的人力與時間成本。
為了要讓機器了解資料中所隱含的意義,達到資料語意上的偵錯,本研究透過本體論提出語意推論架構,針對資料語意上的錯誤,採用專家系統發展程式語言Jess ( Java Expert System Shell ) 撰寫規則,讓機器能理解資料的涵義進行推論,檢查出語意上可能有問題的資料,減少因語意錯誤而導致不必要的損失與檢查過程所需耗費的時間及人力,進而提升工作效率和企業聲譽。
英文摘要 Through the rapid development of internet, website contents also experiencing major changes in time and space. An important question is how to maintain the rationality, correctness and integrity of data. Most websites input data manually by the user, but this may usually result in incorrect data and thus the huge loss. Although it is not difficult to syntactically checking the correctness of the data on website, verifying the semantic meaning of the data involves complexity. Besides, it is also increasing the time and labor cost on maintaining website information.
This research presents a semantic reasoning framework using expert system development program language Jess (Java Expert System Shell) for the reasoning under specific ontology. This allows the machine to understand the meaning of data and detect semantic errors of those data. This approach reduces unnecessary loss due to semantic error and decreasing the time and effort on checking data while promoting the work efficiency and business reputation.
論文目次 第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 論文架構 2
第二章 文獻探討 4
2.1 語意網路 (Semantic Web) 4
2.1.1 語意網路概述 4
2.1.2 語意網路表示方式 4
2.2 本體論 (Ontology) 7
2.2.1 本體論概述 7
2.2.2 本體論組成要素 8
2.2.3 建造本體論之目的 8
2.2.4 領域本體論之架構 10
2.3 專家系統 11
2.3.1 專家系統簡介 11
2.3.2 Jess 13
第三章 資料語意偵錯 16
3.1 系統架構 17
3.2 建置本體論 20
3.2.1 定義本體論領域與相關名詞 20
3.2.2 建立階層、屬性與實例 21
3.3 Jess 語言 24
3.4 整合本體論和Jess 27
第四章 語意偵錯系統 31
4.1 系統使用說明 31
4.2 系統使用結果 33
第五章 結論與未來研究方向 43
5.1 結論 43
5.2 未來研究方向 44
參考文獻 46
附錄、Protégé架構圖 49

圖目錄
圖 1 Triple 5
圖 2 特定本體論的三階層表示圖 10
圖 3 專家系統的組成結構 12
圖 4 規則引擎的組成 14
圖 5 系統架構圖 18
圖 6 推論過程 19
圖 7 資訊商品部分類別圖 22
圖 8 Protégé實作類別架構圖 23
圖 9 光碟機(CD_ROM)之實例 24
圖 10 Jess Function語法 24
圖 11 Jess Function範例 25
圖 12 Jess Rule語法 26
圖 13 Jess Rule範例 26
圖 14 Jess的fact 29
圖 15 建立本體論實例介面 31
圖 16 規則編輯介面 32
圖 17 使用者介面 33
參考文獻 中文文獻
1. 葉文權、民94,應用本體論建構財務報表分析專家系統,國立高雄第一科技大學資訊管理所碩士論文。
2. 簡嘉德、民94,使用本體論輔助維護網頁語意內容正確性之研究,淡江大學資訊管理學系碩士論文。
3. 關銘、民93,以OWL DL及SWRL為基礎建置推論雛型系統-以大學排課問題為例,中原大學資訊管理學系碩士論文。
英文文獻
1. Chandrasekaran, B., Josephson, J. R. and Benjamins, V. R., “What Are Ontologies, and Why Do We Need Them,” IEEE Intelligent Systems, Vol. 14, Iss. 1, 1999, pp. 20-26.
2. CLIPS, A Tool for Building Expert Systems, (http://www.ghg.net/clips/CLIPS.html)
3. CLIPS Reference Manual, Vol. I, Basic Programming Guide, V. 6.24, 2006.
4. Daconta, M. C., Obrst, L. J. and Smith, K. T., “The Semantic Web: A Guide to the Future of XML, Web Services and Knowledge Management,” Wiley Publish, 2003.
5. Davis, R., Buchanan, B. G. and Shortliffe, E. H., “Production Rules as a Representation for a Knowledge-Based Consultation Program,” Artificial Intelligence, No. 8, 1977, pp.15-45.
6. Ding, Y., “Ontology: The enabler for the Semantic Web,” Journal of Information Science,” Vol. 27, No. 6, 2001, pp. 377-384.
7. Eriksson, H., “JessTab Manual --- Integration of Protégé and Jess,” http://www.ida.liu.se/~her/JessTab, Linköping University, 2004.
8. Eriksson, H., “Using JessTab to Integrate Protégé and Jess,” IEEE Intelligent Systems, Vol. 18, Iss. 2, 2003, pp. 43-50.
9. Friedman-Hill, E., “Jess in Action:Rule-Based Systems in Java,” Manning Publications , 2003
10. Friedman-Hill, E., “Jess, The Rule Engine for the Java Platform,” http://herzberg.ca.sandia.gov/jess/, Nov. 21, 2003.
11. Giarratano, J. C., and Riley, G. D. “Expert Systems: Principles and Programming,” PWS Publishing, 1998.
12. Gruber, T. R., “A Translation Approach to Portable Ontology Specifications,” Knowledge to Acquisition, Vol. 5, Iss. 2, 1993, pp.199-220.
13. Gruber, T. R., “Towards Principles for the Design of Ontologies Used for Knowledge Sharing,” International Journal of Human-Computer Studies, Vol. 43, Iss. 5-6, 1995, pp.907-928.
14. Horrocks, I., Patel-Schneider, P. F. and Harmelen, F. V., “From SHIQ and RDF to OWL: The Making of a Web Ontology Language,” Journal of Web Semantics, Vol. 1, No. 1, 2003, pp. 7-26.
15. Jovanovic, J., Gasevic, D. and Devedzic, V. “A GUI for Jess,” Expert Systems with Applications, Vol. 26, No. 4, 2004, pp.625-637.
16. Lindström, B., “Creative Commons for Corpus Construction,” Department of Linguistics and Philology, Uppsala university.
17. Manola, F. and Miller, E., “RDF Primer,” W3C Recommendation, 2004.
18. McGuinness, D.L. and Wright, J. R., “Conceptual Modeling for Configuration: A Description Logic-based Approach,” Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, Vol. 12, Iss. 4, 1998, pp.333-344.
19. McGuinness, D.L., Fikes, R., Rice, J. and Wilder, S., “An Environment for Merging and Testing Large Ontologies, ” Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning, 2000.
20. Musen, M. A., “Dimensions of Knowledge Sharing and Reuse, ” Computers and Biomedical Research, Vol. 25, Iss. 5, 1992, pp. 435-467.
21. Nault, B. R. and Storey, V. C., “Using Object Concepts to Match Artificial Intelligence Techniques to Problem Types,” Information and Management, Vol. 34, Iss. 1, 1998, pp.19-31.
22. Navigli, R., Velardi, P. and Gangemi, A., “Ontology Learning and Its Application to Automated Terminology Translation,” IEEE Intelligent Systems, Vol. 18, Iss. 1, 2003, pp.22-31.
23. Negnevitsky, M., “Artificial Intelligence-A Guide to Intelligent Systems,” Addison Wesley, 2002.
24. Noy, N. F. and McGuinness, D. L., “Ontology Development 101: A Guide to Creating Your First Ontology,” Stanford Knowledge Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, 2001.
25. Noy, N. F., Sintek, M., Decker, S., Crubézy, M., Fergerson, R. W. and Musen, M. A., “Creating Semantic Web Contents with Protégé-2000,” IEEE Intelligent Systems, Vol. 16, Iss. 2, 2001, pp. 60-71.
26. Rothenfluh, T. E., Gennari, J. H., Eriksson, H., Puerta, A.R., Tu, S.W. and Musen, M.A., “Reusable Ontologies, Knowledge-Acquisition Tools, and Performance Systems: PROTÉGÉ-II Solutions to Sisyphus-2,” International Journal of Human-Computer Studies, Vol. 44, 1996, pp.303-332.
27. Ruckhaus, E. and Vidal, M. E., “XWebSOGO: An Ontology Language to Describe and Query Web Sources,” Workshop On Web Information And Data Management, 2003, pp.62-65.
28. Shortliffe, E. H., “Computer-Based Medical Consultations : MYCIN,” Elsevier, 1976.
29. Speel, P-H., Schreiber, A. Th., van Joolingen, W., van Heijst, G. and Beijer, G. J. “Conceptual Modelling for Knowledge-Based Systems,” Encyclopedia of Computer Science and Technology, 2001.
30. The Protégé Project, http://protege.stanford.edu
31. W3C Consortium, Extensible Markup Language(XML) (http://www.w3.org/)
32. W3C Recommendation, RDF Primer, (http://www.w3.org/TR/rdf-primer/)
33. W3C Recommendation, OWL Web Ontology Language Reference, (http://www.w3.org/TR/owl-ref/)
34. W3C Semantic Web , (http://www.w3.org/2001/sw/)
35. W3C Semantic Web Activity Statement, 2001. (http://www.w3.org/2001/sw/Activity)
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