臺灣博碩士論文加值系統

在此論文我們指出五項處理AND與OR模糊資訊檢索操作之現存平均操作元(i.e., P-Norm操作元、Infinite-One操作元、Waller-Kraft操作元、Geometric-Mean Averaging操作元、Weighed Power-Mean Averaging操作元)一些缺失。此外，我們利用平方平均數(Quadratic-Mean)提出一項新型態之平均操作元稱之為平方平均數操作元(Quadratic-Mean Averaging operators)，進而克服現存模糊資訊檢索平均操作元缺失。我們也對於平方平均數操作元證明特性，並且利用範例針對平方平均數操作元與現存平均操作元之間進行比較。平方平均數操作元可以克服現存平均操作元缺失，並且可以簡易地決定處理AND與OR模糊資訊檢索操作之參數值α。
但是，平方平均數(QMA)操作元不能處理一般化模糊數(Generalized Fuzzy Numbers)以及區間值模糊數(Interval-Valued Fuzzy Numbers)呈現之詢問。因此，在此論文我們提出一般化模糊數平方平均數操作元(Generalized Fuzzy-Number Quadratic-Mean Averaging operators; GFNQMA)進而處理一般化模糊數與區間值模糊數呈現之詢問。一般化模糊數平方平均數操作元處理一般化模糊數與區間值模糊數呈現之詢問的模糊資訊檢索操作更為具有彈性更為具有幫助。
此外，由於上述資訊檢索運算當中，於關鍵字與關鍵字之間處理AND與OR之模糊資訊檢索操作僅僅具有一項模糊關係(i.e., 模糊正向關係)。因此，在此論文我們也提出一項在延伸模糊概念網路(Extended Fuzzy Concept Networks)之下處理文件檢索模糊詢問流程之新型態架構。其中，延伸模糊概念網路為概念與概念之間具有四項模糊關係，分別為模糊正向關係(Fuzzy Positive Association)、模糊負向關係( Fuzzy Negative
Association)、模糊一般化關係(Fuzzy Generalization)、模糊特殊化關係(Fuzzy Specialization)。並且一個延伸模糊概念網路可以利用相關矩陣(Relevance Matrix)與關係矩陣(Relation Matrix)之模型所構成。其中，相關矩陣為概念之間之相關程度而關係矩陣為概念之間之模糊關係。概念之間更進一步的內隱相關程度可以利用相關矩陣遞移包(Transitive Closure of the Relevance Matrix)進而推斷；而概念之間更進一步的內隱模糊關係可以利用關係矩陣遞移包(Transitive Closure of the Relation Matrix)進而推斷。此新型態架構模型可以克服現存文件檢索系統在延伸模糊概念網路之下之缺失。因此，此新型態架構模型在處理文件檢索問題是更為具有彈性更為具有幫助。
最後，我們也呈現一項在延伸模糊數概念網路之下處理文件檢索模糊數詢問流程之型態架構，進而處理一般化模糊數以及區間值模糊數呈現之語意詢問。此型態架構模型在處理一般化模糊數與區間值模糊數文件檢索問題是更為具有彈性更為具有幫助。

In this thesis, we indicate that some drawbacks of the five existing averaging operators (i.e., P-Norm operators, Infinite-One operators, Waller-Kraft operators, Geometric-Mean Averaging operators, and Weighed Power-Mean Averaging operators) for handling AND and OR operations of fuzzy information retrieval. Furthermore, we present new averaging operators based on quadratic-mean to overcome these drawbacks for fuzzy information retrieval. We also prove some properties of the proposed averaging operators, and use some examples to compare the proposed averaging operators with the existing averaging operators. The proposed averaging operators can overcome the drawbacks of the existing averaging operators, and easily determine an appropriate value for the parameter α for handling AND and OR operations of fuzzy information retrieval.
However, the QMA operators can not deal with queries represented by generalized fuzzy numbers and interval-valued fuzzy numbers. In this thesis, we present generalized fuzzy-number quadratic-mean averaging (GFNQMA) operators for dealing with queries based on generalized fuzzy numbers and interval-valued fuzzy numbers. The proposed GFNQMA operators are more flexible and more useful to deal with generalized fuzzy numbers and interval-valued fuzzy numbers queries.
Moreover, the above researches are all dealing with only one kind of fuzzy relationship between keywords, i.e., fuzzy positive association relation. Hence, we also propose a new architecture for fuzzy query processing for document retrieval based on extended fuzzy concept networks. In an extended fuzzy concept network, there are four kinds of fuzzy relationship between concepts in the concept networks, i.e., fuzzy positive association, fuzzy negative association, fuzzy generalization, fuzzy specialization. An extended fuzzy concept network can be modeled by a relevance matrix and a relation matrix where the elements in a relevance matrix represent the degrees of relevance between concepts and the elements in a relation matrix represent the fuzzy relationships between concepts. The implicit degrees of relevance between concepts can be inferred by the transitive closure of the relevance matrix. The implicit fuzzy relationships between concepts also can be inferred by the transitive closure of the relation matrix. The proposed method can overcome drawbacks of the existing document retrieval systems based on extended fuzzy concept networks. Hence, the proposed method is more flexible and more useful fuzzy information retrieval method to deal with document retrieval.
Finally, we also present a architecture of fuzzy number query processing for document retrieval based on extended fuzzy number concept networks to deal with linguistic queries represented by generalized fuzzy numbers and interval-valued fuzzy numbers. The presented method is more flexible and more useful to deal with generalized fuzzy numbers and interval-valued fuzzy numbers queries.

CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Organization of This Thesis 5
CHAPTER 2 PRELIMINARY 8
2.1 Quadratic Mean 8
2.2 Information Retrieval Based on the Conventional Fuzzy Set Model 8
2.3 Some Existing T-Operators 9
2.4 Some Existing Averaging Operators 10
2.5 The Operator Graphs and Some Analytic Results of the Existing T-Operators and Averaging Operators 14
2.6 Generalized Fuzzy Numbers and Interval-Valued Fuzzy Numbers 19
2.7 The Ranking Method for Generalized Trapezoidal Fuzzy Numbers and Interval-Valued Fuzzy Numbers 22
2.8 Concept Networks and Extended Fuzzy Concept Networks 22
CHAPTER 3 QUADRATIC-MEAN AVERAGING OPERATORS AND GENERALIZED FUZZY NUMBER QUADRATIC-MEAN AVERAGING OPERATORS 32
3.1 Analysis of the Existing Fuzzy Sets Averaging Operators 32
3.2 Fuzzy Information Retrieval Based on the Proposed Quadratic-Mean Averaging Operators 38
3.3 Weighted Fuzzy Query Based on the Extended Quadratic-Mean Averaging Operators 50
3.4 Analysis of the Existing Fuzzy Numbers Averaging Operators 53
3.5 Using Generalized Fuzzy-Number Quadratic-Mean Averaging (GFNQMA) Operators to Deal With Queries Represented by Generalized Fuzzy Numbers 58
3.6 Using Generalized Fuzzy-Number Quadratic-Mean Averaging (GFNQMA) Operators to Deal With Queries Represented by Interval-Valued Fuzzy Numbers 71
CHAPTER 4 A NEW ARCHITECTURE OF FUZZY QUERY PROCESSING FOR DOCUMENT RETRIEVAL BASED ON EXTENDED FUZZY CONCEPT NETWORKS 84
4.1 Analysis of the Existing Document Retrieval Based on Extended Fuzzy Concept Networks 84
4.2 A Proposed Architecture for Document Retrieval Method Based on Extended Fuzzy Concept Networks 87
4.3 A Architecture of Fuzzy Number Query Processing for Document Retrieval Based on Extended Fuzzy Number Concept Networks to Deal With Queries Represented by Generalized Fuzzy Numbers 106
4.4 Fuzzy Number Query Processing for Document Retrieval Based on Extended Fuzzy Number Concept Networks to Deal With Queries Represented by Interval-Valued Fuzzy Numbers 122
CHAPTER 5 CONCLUSIONS 129
REFERENCES 131

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