臺灣博碩士論文加值系統

在這篇論文中，提出了一個以知識本體論為基礎的模糊推論（Ontology-based fuzzy inference）架構，簡稱為「OFI」，並以半導體製程診斷作為應用問題。其設計目標在於期望將所有的知識含蓋於OFI 運行流程之中：著眼於不斷的演進具彈性、可擴充性；其他系統可以分享與重複使用；對於不同的應用，可依需求隨時自行組裝使用。

OFI架構分為兩個部份：integrated OFI ontology與configurable OFI system。integrated OFI ontology編輯可分享的Web Ontology Language（OWL），來描述包含於OFI process中的Knowledge。相較於其他的ontology設計，integrated OFI ontology不僅僅只設計了事實與規則的ontology，同時也設計了流程與方法的ontology來表示推論的步驟。此外，integrated OFI ontology還具備了表達模糊理論的能力，並能有效的表示不確定性的知識。

configurable OFI system是用JAVA程式語言所開發而成的。在系統的運作階段（run-time phase），實際操作與控制integrated OFI ontology的運行。

借由OWL概念與JAVA物件之間的轉換設計（OWL-concept-to-JAVA-object transfer），在系統的運作階段，integrated OFI ontology中所描述之各個不同的Knowledge與JAVA物件之間可以被動態的轉換與存取。

基於OFI架構，本論文設計實作了一具max-min fuzzy logic運算的OFI系統，並於文中描述其詳細的過程與步驟，並且以半導體的製程診斷為例，示範OFI系統的運作，此外，也引用了兩個評估的案例來驗證OFI架構的設計目標。

This thesis proposes an ontology-based fuzzy inference (OFI) architecture using the semiconductor manufacturing process diagnosis as a driving problem. The design objective is to make all the knowledge involved in OFI process: flexible and scalable with respect to continuous evolutions, shareable and reusable by other systems, and configurable for adapting to various applications.

The OFI architecture is composed of two parts: integrated OFI ontology and configurable OFI system. The integrated OFI ontology adopts the sharable Web Ontology Language (OWL) to describe all the knowledge involved in OFI processes. As compared to other designs of ontology, the integrated OFI ontology not only designs fact and rules ontology, but also designs flow ontology and function ontology to present inference procedure. Moreover, the integrated OFI ontology has the capability to model fuzzy logic theory and present vagueness knowledge.

The configurable OFI system is constructed by JAVA programming to manipulate the integrated OFI ontology at the system run-time phase. An OWL-concept-to-JAVA-object transformer is designed and implemented so that it can access and dynamically transfer various knowledge sources described in integrated OFI ontology to corresponding JAVA objects during the run-time phase.

On top of the OFI architecture, the procedures to design and implement a real OFI system with the mechanism of max-min fuzzy logic inference are proposed and demonstrated by its applications to semiconductor manufacturing process diagnoses. Two evolutions of the max-min fuzzy logic inference mechanism are then designed and implemented to validate the original design objectives.

Acknowledgements .......................................................................................................i
摘 要.........................................................................................................................ii
Abstract.......................................................................................................................iv
Contents .......................................................................................................................vi
List of Tables............................................................................................................. viii
List of Figures..............................................................................................................ix
Chapter 1 Introduction..........................................................................................1
Chapter 2 Literature survey .................................................................................5
2.1 Knowledge Representation ..............................................................................5
2.2 Rule-Based Expert System ..............................................................................9
2.3 Ontology ........................................................................................................11
2.4 Integration of Ontology &; Expert Systems ...................................................13
2.5 Intelligent Process Diagnosis System............................................................19
Chapter 3 OFI System Architecture Design ......................................................22
3.1 Overall Architecture.......................................................................................22
3.2 Simple Example Demonstrations...................................................................23
3.3 Design of Flow Ontology ..............................................................................25
3.4 Function Ontology Designed .........................................................................30
3.5 Data Ontology Design....................................................................................33
3.6 Java Implementation Procedure.....................................................................37
Chapter 4 Ontology-Based Fuzzy Inference System (OFI)..............................42
4.1 OFI Function Decomposition and Analysis...................................................42
4.2 Fuzzy function Input/Output and Logic Analysis..........................................43
4.3 Construct OFI System Ontology....................................................................58
4.3.1 Function Ontology ..............................................................................58
4.3.2 Data Ontology.....................................................................................63
vii
4.5 Flow Ontology ...............................................................................................73
4.6 Overall ontology ............................................................................................74
Chapter 5 System Implementation &; Validation ..............................................75
5.1 OFI System Implementation ..........................................................................75
5.2 Hardware and software configuration............................................................75
5.3 Use Protégé to Create Ontology Model ........................................................76
5.4 Use JAVA to Create Configurable OFI System .............................................77
5.5 System Configuration &; Implementation......................................................78
Chapter 6 OFI Scalability Demonstration.........................................................84
6.1 Flexibility, Scalability Demonstration ...........................................................84
6.2 Shareability, Reusability Demonstration .......................................................92
Chapter 7 Conclusion &; Future Work...............................................................94
7.1 Conclusion .....................................................................................................94
7.2 Future Work ...................................................................................................94
Reference ....................................................................................................................96

1. 關銘，”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”，碩士論文，中原大學，2004.
2. CLIPSTab Documentation, <
http://protege.stanford.edu/plugins/CLIPSTabPages/CLIPS_tab.html>
3. Fuzzy logic Toolbox,
<http://www.mathworks.com/access/helpdesk/help/toolbox/fuzzy/fuzzy_product_pa
ge.html>
4. Giarratano J.C. “CLIPS (C Language Integrated Production System) User's Guide” ,
< http://www.cs.oswego.edu/~odendahl/manuals/clips/userguide/>, 2002
5. Protégé-OWL API Programer’s Guide,
<http://protege.stanford.edu/plugins/owl/api/guide.html l>
6. Brachman R.J., McGuinness D.L., Resnick L.A., Borgida A., “Living with
CLASSIC: When and How Use a KL-ONE-Like Language”, Appears in Principles
of Semantic Networks: Explorations in the Representation of Knowledge, pp
401-456. 1991
7. Baader F., McGuinness D.L., Nardi D., “The Description Logic Handbook: Theory,
Implementation and Applications”, Cambridge University Press, 2003.
8. DRIANKOV D., Hellendoorn H., “AN INTRODUCTION TO FUZZY CONTROL”,
BERLIN SPRINGER-VERLAG, 1995.
9. Lin Y.T., Tseng S.S., Tsai C.F., “Design and Implementation of a new
Object-oriented Rule Base platform, National”, Expert systems with Applications,
Vol. 25, pp369-385, 2003.
10. Wu X., Cai K., “Knowledge Object Modeling”, IEEE transactions on systems, man,
and cybernetics, Vol. 30 No. 2, 2000.
97
11. Shortliffe E.H., Buchanan B.G., “A Model of Inexact Reasoning in Medicine”,
Mathematical Biosciences, Vol. 23, pp 350-397, 1975.
12. Tsai T.N., Te S. “A Knowledge-Based System for Stencil Printing Process Planning
and Control”, Journal of the Chinese Institute of Industrial Engineers, Vol. 24 No. 6,
pp 513-521, 2007.
13. Lassila O., McGuinness D.,” The Role of Frame-Based Representation on the
Semantic Web”, Software Technology Laboratory, Nokia Research Center.
14. Guo R.S., Tsai C.K., Lee J.H., Chang S.C., “Intelligent Process Diagnosis Based on
End-of-Line Electrical Test Data”, IEEE/CPMT Int’l Electronics Manufacturing
Technology Symposium, 1996.
15. Brachman R.J., Levesque H.J., “Knowledge Representation and Reasoning”,
Elsevier, 2004.
16. Sowa J.F., “Knowledge Representation Logical, Philosophical, and Computational
Foundations”, Brooks/Cole, 2000.
17. Russell S., Norvig P., “Artificial Intelligent Modern Approach”, Person Education
Inc., 2003.
18. Ross T.J., “FUZZY LOGIC WITH ENGINEERING APPLICATIONS”, NEW
YORK MCGRAW-HILL, 1995.
19. Minsky M., “A framework for representing knowledge”. In P.H. Winston (ed.), The
Psychology of Computer Vision, pp 211-277, 1975.
20. Fikes R., Kehler T., “The Role of Frame-Based Representation in Reasoning”,
CACM, pp 904-920. 1985.
21. Zimmermann H.J., “Fuzzy set theory—and its applications”, Kluwer Academic
Publishers, 1996.