臺灣博碩士論文加值系統

「本體論」(Ontology)源於哲學領域，其解釋為有系統且存在的事物。而正規化概念分析法(Formal Concept Analysis；FCA)可豐富知識架構，能夠明確定義類別與概念，也可從資料中擷取出概念層級，適用於手動或半自動發展本體論，而且最重要的是FCA可以當作一個找出屬性(attribute)相依的機制；另一方面，鑑於OWL語言為目前知識表達較完整的語言，因此可以利用OWL語言描述穴位知識，並以Racer推理機加以推論，以驗證知識建構的一致性；再者，因為每一個穴位都有特定的治療效果、按摩方法等相同或不同的屬性，而穴位彼此之間擁有錯綜複雜的關係，因此找出一個適切的方法論來建構穴位本體論確實有其必要性。
有鑑於此，本研究將擷取正規化概念分析法(FCA)客觀條件之優點並且結合根據IEEE軟體發展標準為基礎的本體論建構活動所發發展的TOVE本體論工程，同時使用最新的本體論語言OWL以提高本體論建構之各項優勢並且將之應用於穴位按摩領域知識本體論的建構上。而所建構穴位按摩領域知識本體論將包括知識描述完整的穴位按摩知識之優點；此外在穴位按摩本體論的推論上也產生新資訊，具有豐富的推論結果。本研究結果即預期能讓此本體論成為穴位領域的典範，而所建構之領域本體論最後將訂定SWRL之規則語言以詢問本體論問題解決之道並藉此推論以產生新資訊。
在研究貢獻上，本研究將可以提供一般大眾作為學習的穴位按摩網站平台之外，亦可以安裝於個人電腦之應用軟體使用或作為學習穴位按摩之用。

This study of ontology engineering constructs ontology to be a knowledge base. We propose the methodology of ontology engineering with philosophy and aesthetic. Formal Concept Analysis (FCA) is a key point to construct acupoint ontology. FCA not only makes acupoint ontology abundantly, but also helps us to well define the concept of the class. On the other hand, FCA could retrieve the hierarchical concept because of finding the common attribution among the classes. In addition, we follow the TOVE ontology engineering development process of constructive activity. It is base on the standard of IEEE software development. This study also uses the Ontology Web Language Descript Language (OWL DL) to describe the knowledge of acupoint because OWL DL could reason the logic of ontology by Racer reasoned. The Racer reasoner checks the logical consistency of the ontology. We also use SWRL rule language that supports a rule engine which provides for new information after reasoning. Therefore, our ontology constructed by using FCA, TOVE ontology engineering and the OWL DL. Finally, we use SWRL to ask some question to answer by ontology. This aesthetic ontology contains the well defined term, and completely constructs the knowledge framework, so we expect the programmer or human-and-machine interface designer could use this ontology to build a website to everyone. It also helps doctor to diagnose the patient.

中文摘要 I
英文摘要 II
目錄 III
表目錄 V
圖目錄 VI
第一章 緒 論 1
1.1. 研究背景與動機 1
1.2. 研究目的 3
1.3. 研究範圍與資料蒐集 4
1.4. 研究限制 4
1.5. 論文架構 5
第二章 文獻探討 7
2.1 本體論 7
2.1.1 本體論的定義 7
2.1.2 OWL本體論語言 9
2.2 本體論工程 12
2.2.1 本體論的活動發展程序 12
2.2.2 相關專案之本體論工程 15
2.2.3 本體論開發工具與推論工具 19
2.2.4 使用RacerPro 9.0之相關研究 22
2.2.5 語意網規則語言 23
2.3正規化概念分析(FORMAL CONCEPT ANALYSIS) 27
2.3.1 正規化概念分析法之定義 27
2.3.2 正規化概念分析法之相關研究 29
2.4 穴位按摩 31
2.4.1 穴位的定義 31
2.5.3 穴位按摩的原理 32
第三章 研究方法 34
3.1 研究流程 34
3.2 穴位按摩本體論工程 37
3.2.1 Motivating Scenario 37
3.2.2 Informal Competency Question 39
3.2.3 Terminology 40
3.2.4 Formal Competency Question 43
3.2.5 Axioms 49
3.2.6 Completeness Theorems 54
第四章 領域知識本體論實作 55
4.1 本體論領域範圍的定義 55
4.1.1 穴位位置之概念階層 55
4.1.2 按摩功效之概念階層 57
4.1.3 按摩方法之概念階層 60
4.2 定義屬性關係與實例 61
4.2.1 實體知識、屬性關係與類別限制之實作 61
4.2.2限制式與SWRL規則實作 68
4.2.3 本體論之驗證 70
4.3推論結果 76
4.3.1 基本Rule之推論 76
4.3.2 按摩步驟的推論 77
4.3.3 新資訊的推論 81
4.4專家評估 83
第五章 研究結論與貢獻 86
5.1 研究結論 86
5.2 研究貢獻 89
5.2.1 描述完整的穴位按摩領域本體論 89
5.2.2 拓展應用本體論新領域 91
5.3 未來展望 94
參考文獻 95
附錄A 100
附錄B 102

英文部分
1.Asuncion, G. –P., and Mariano, F. –L. (2004). Ontology Engineering with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. (2nd In.) Springer-Verlag London Limited.
2.Belen, D. -A., Pedro, A. G. -C. (2001). Formal concept analysis as a support technique for CBR. Knowledge-Base Systems, 14. 163-171.
3.Berardi, D., Calvanese, D., and Giacomo, D. G. (2005). Reasoning on UML class diagrams. Artifical Intelligence 168. 70 - 118
4.Borst, P., Akkermans, H., and Top, J. (1997). Engineering Ontologies. Int. J. Human-Computer Studies, 46. 365-406.
5.Cimiano, P., Hotho, A., Stumme, G., and Tane, J. (2004). Conceptual Knowledge Processing with Formal Concept Analysis and Ontologies. Second International Conference on Formal Concept Analysis, ICFCA. 189-207
6.Corcho, O., Fernandez, -L, M., and Gomez, -P, A. (2003). Methodologies, tools and languages for building ontologies. Where is their meeting point? Data and Knowledge Engineering 46. 41-64.
7.Everts, J. T., Park, S. S., and Kang, H. B. (2006). Using Formal Concept Analysis with an Incremental Knowledge Acquisition System for Web Document Management. Vladimir, E. –C., and Dobbie, G. (Ed.), Conferences in Research and Practice in Information Technology(CRPIT), Vol. 48. The Twenty-Ninth Australasian Computer Science Conference.
8.Formica, A. (2006). Ontology-based concept similarity in Formal Concept Analysis. Information Science 176. 2624-2641.
9.Grau, C. B., Parsia, B., and Sirin, E. (2006). Combining OWL ontologies using ε-Connections. Web Semantics: Services and Agents on the World Wide Web 4, 40-59.
10.Horrocks, I., Patel-Schneider P. F., Boley, H., Tabet, S., Grosof, B., and Dean, M (2004) SWRL: A Semantic Web Rule Language Combining OWL and RuleML. National Research Council of Canada, Network Inference, and Stanford University.
11.Huang, N. and Diao, S. (2008). Ontology-base enterprise knowledge integration. Robotics and Computer-Integrated Manufacturing 24, 562-571.
12.Jiang, G., Ogasawara, K., Endoh, A., and Sakurai, T. (2003). Context-based ontology building support in clinical domains using formal concept analysis. International Journal of Medical Informatics 71. 71-81.
13.Kim, L. H., Hwang, H. S., and Kim, G. H. (2007). FCA-based Approach for Mining Contextualized Folksonomy. Social and Behavioral Science. 1340-1346.
14.Lopez, F. M. (1999). Overview Of Methodologies For Building Ontologies. Proceedings of the IJCAI-99 workshop on Ontologies and Problem-Slving Methods(KRR5) Stockholm, vol-18. 4.1-4.13.
15.Luther, M., Mrohs, B., Wagner, M., Steglic, S., and Kellerer, W. (2005). Situational Reasoning - A Practical OWL Use Case. Autonomous Decentralized Systems, 2005. ISADS 2005. Proceedings. 461- 468.
16.Mizoguchi and Ikeda. (1996). Towards Ontology Engineering (Technical Report AI-TR-96-1, ISIR). The Institute of Scientific and Industrial Research, Osaka University, 567 Japan.
17.Noy, F. N., and McGuinness, L. D. Ontology Development 101: A Guide to Creating Your First Ontology. Standord niversity, Stanford, CA, 94035.
18.Racer Systems GmbH and co. KG. (2005). RacerPro User''s Guide Version 1.9. Unpublished manuscript.
19.Schulz, S. and Hahn, U. (2005). Part-whole representation and reasoning in formal biomedical ontologies. Artifical Intelligence in Medicine 34. 179 – 200.

20.Sowa, J. F. (2005). A semantic network is a graphic notation for representing knowledge in patterns of interconnected nodes and arcs. Encyclopedia of cognitive science v4. 1082.
21.Stuckenschmidt, H., and Klein, M. (2007). Reasoning and change management in modular ontologies. Data and Knowledge Engineering 63, 200-223.
22.Studer, R., Benjamins, V. R., and Fensel, D. (1998). Knowledge Engineering: Principles and methods. Data and Knowledge Engineering, 25. 161-197.
23.Using Formal Concept Analysis (FCA) for Ontology Structuring and Building. (nd). Unpublished manuscript.
24.Wang, H. H., Li, F. Y., Sun, J., Zhang, H., and Pan, J. (2007). Verifying feature models using OWL. Web Semantics: Services and Agents on the World Wide Web 5, 117-129.
25.Warren, P. and Alsmeyer, D. (2005). Applying semantic technology to a digital library: a case study. Library Management, 26. 196-205.
26.Yu, J., Thom, A. J., and Tam, A. (2007) Ontology Evaluation Using Wikipedia Categories for Browsing. ACM Sixteenth Conference on Information and Knowledge Management (CIKM).

中文部分
1.江駿然、周正邦、白聖琳、楊建中、詹金淦 (2005)，對症按摩圖典，三采文化出版事業有限公司。
2.張清忠 (2007)，人體使用手冊，達觀出版事業有限公司。
3.陳文賢 (2000)，資訊管理，東華書局。
4.陳宏強 (2004)，臟腑經絡按摩法，五州出版有限公司。

網路部分
1.Ontology Development 101: A Guide to Creating your First Ontology. Retrieved November 19, 2007, from http://protege.stanford.edu/
2.RacerPro documentation. Retrieved November 19, 2007, from http://www.racer-systems.com/
3.TOVE Ontology Project. Retrieved November 19, 2007, from http://www.eil.utoronto.ca/enterprise-modelling/tove/index.html
4.Distributed AI. Retrieved November 19, 2007, http://www.cyc.com/
5.John F. Sowa website. Retrieved November 23, 2007, from http://www.jfsowa.com/ontology/index.htm
6.SWRLJessTab. Retrieved May 8, 2008, from http://protege.cim3.net/cgi-bin/wiki.pl?action=browse&id=SWRLJessTab&revision=19
7.SWRL: A Semantic Web Rule Language Combining OWL and RuleML. Retrieved May 8, 2008, from http://www.w3.org/Submission/2004/SUBM-SWRL-20040521/