臺灣博碩士論文加值系統

目前案例式推理系統被廣泛的應用在各領域中。但開發案例式推理系統需要長久的時間來設計、規劃，因此我們希望可以發展一個系統來幫助開發者設計案例式推理系統，使開發者節省系統開發時間，並且由於我們的系統是物件導向結構，所以開發者可以更容易的修改原案例式推理系統成為新的領域，這使得我們的系統可以被重復的被利用在不同領域。而我們的系統運用了XML的資料繫結技術來幫助將案例式推理系統的資訊結構化，並將案例資訊轉換成XML格式的檔案，使開發者可以透過修改XML檔案即可以改變案例式推理系統的應用領域和案例結構。我們也運用了WordNet以及語意相似度的研究來幫案例式推理系統可以逹到語意階層的比較，並且針對不同的案例特徵資料型態提供不同的相似度比較方法。最後我們也利用一個吃比薩的例子來實際操作我們的系統。

The case-based reasoning (CBR) systems apply every field to extensive application at present. but develop the CBR system need a long time to design and plan, so we hope to develop a system and help developers to design the CBR system, make developers can save system to develop period, and because our system is the object-oriented structure, so developer can easy modification original CBR system become new field, this makes our system can be utilized in different field by the repeated one. and system of us use data binding of XML to form technology is it help information of CBR system to structured, and change the information of the case into a file of XML, it is field changing the CBR system and case structure to enable developers to revise XML file. We use WordNet and semantic similarity research which helps CBR system to semantic comparison, and direct different data type of case attribute to offer different similar degree of comparative methods. Finally we utilize an example of eating Pizza to our system of practical operation comes.

第一章 導論 1
第二章 相關背景知識 3
2.1 jCOLIBRI之概念研究 3
2.2 語意相似度研究 5
2.3 小結 9
第三章 研究方法 10
3.1 研究動機 10
3.2 案例擷取方法 10
3.2.1 縮短相似度測量時間方法 10
3.3 相似度測量 12
3.3.1 物件相似度比較函式 13
3.3.2 間距相似度比較函式 14
3.3.3 字串相似度比較函式 14
3.3.4 語意相似度比較函式 15
3.4 案例適應方法 16
3.5 XML在系統中的運用 18
3.5.1 Case Structure 19
3.5.2 Case Base 20
3.5.3 Connector 20
第四章 實作 24
4.1 系統架構 24
4.1.1 CBR Frames Generation 24
4.1.2 CBR Core 25
4.2. 分析與設計 27
4.2.1 UML 說明 27
4.2.2 外部工具說明 33
4.3 實作結果 34
4.3.1 案例式推理系統開發工具操作流程 34
4.3.2 案例式推理系統操作流程 37
4.3.3 Eat Pizza 實例 38
4.4 系統比較 42
第五章 結論與未來展望 45
附錄A 46
A.1 案例式推理 46
A.1.1 案例式推理之運作過程 46
A.1.2 案例描述 48
A.1.3 案例擷取 48
A.1.4 案例適應 49
A.1.5 案例維護 53
附錄B 54
B.1 XML的技術研究 54
B.1.1 XML Document Type Definition 55
B.1.2 XML Schema 55
B.1.3 Data Binding 55
B.2 XML Schema定義 56
附錄C 61
參考文獻 62

[1].J. M. Corchado, J. Pavón, E. Corchado, L. F. Castillo, “ Development of CBR-BDI Agents: A Tourist Guide Application,” In 7th European Conference on Case-based Reasoning, pp. 547-559, 2004.
[2].J. A. R. Garcia, A. Sánchez, B. Díaz-Agudo, P. A. González-Calero, “jCOLIBRI 1.0 in a nutshell. A software tool for designing CBR systems,” In M. Petridis, editor, Proccedings of the 10th UK Workshop on Case Based Reasoning, pp. 20-28, 2005.
[3].P. A. Gonzalez-Calero, B. Diaz-Agudo, J. J. Bello, J. A. Recio, A. A. S. Granados, P. J. B. Ferruz, JColibri, http://gaia.fdi.ucm.es/grupo/projects/jcolibri/index.html, 2006.
[4].P. Resnik, “Using information content to evaluate semantic similarity in a taxonomy,” in Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 448–453, 1995.
[5].J. J. Jiang, D. W. Conrath, “Semantic similarity based on corpus statistics and lexical taxonomy,” In Proceedings of International Conference on Research in Computational Linguistics, Taiwan, 1997.
[6].N. Seco, T. Veale, J. Hayes, “An Intrinsic Information Content Metric for Semantic Similarity in WordNet,” In Proceedings of the 16th European Conference on Artificial Intelligence, Valencia, Spain, 22–27 August, pp. 1089-1090, 2004.
[7].P. Zhang, “The research and implementation of Semantic Based Web Services Discovery,” Knowledge Engineering Group, Tsinghua University, pp. 36-45, 2005.
[8].M. Menken, “Jess Tutorial,” Vrije Universiteit, Amsterdam, The Netherlands, pp. 1-57, 2002.
[9].E. F. Hill, “Jess in Action: Rule-Based Systems in Java,” Manning Publications, pp. 1-480, 2003.
[10].A. Sánchez, J. A. Recio, B. Díaz-Agudo, P. González-Calero, “Case structures in jCOLIBRI,” Best Poster Award. Twenty-fith SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intelligence, AI. Cambridge, UK, pp. 1-13, 2005.
[11].H. Eriksson, “JessTab Manual: Integration of Protégé and Jess,” Linkoping University, pp. 1-19, 2004.
[12].A. Rector, N. Drummond, M. Horridge, J. Rogers, H. Knublauch, R. Stevens, H. Wang, C. Wroe, ”OWL Pizzas: Practical Experience of Teaching OWL-DL: Common Errors & Common Patterns,” 14th International Conference on Knowledge Engineering and Knowledge Management (EKAW), Whittlebury Hall, UK, 2004.
[13].D. B. Leake, “CBR in Context: the present and future,” In Leake, D., editor, Case-Based Reasoning: Experiences, Lessons, and Future Directions. AAAI Press/MIT Press, pp. 1-30, 1996.
[14].A. Aamodt, E. Plaza, “Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches,” AI Communications, IOS Press, 7(1), pp.39-59, 1994.
[15].W. Wilke, R. Bergmann, “Techniques and Knowledge used for Adaptation during Case-Based Problem Solving,” 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Vol. 2, pp.497-506, Castellón, Spain, 1998.
[16].R. Bergmann, W. Wilke, “On The Role of Abstraction in Case-Based Reasoning,” Proceedings of the Third European Workshop on Advances in Case-Based Reasoning, pp.28-43, Lausanne, Switzerland, 1996.
[17].W3C, Extensible Markup Language (XML), http://www.w3.org/XML/, 2006.
[18].T. Bray, J. Paoli, C. M. Sperberg-McQueen, E. Maler, Extensible Markup Language (XML) 1.0 (Second Edition), http://www.w3.org/TR/2000/REC-xml-20001006, 2000.
[19].D. Mertz, XML Matters: Comparing W3C XML Schemas and Document Type Definitions (DTDs), http://www-128.ibm.com/developerworks/library/x-matters7.html#author, 2001.