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Knowledge Integration Methods for Probabilistic Knowledge-based Systems [Kietas viršelis]

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  • Formatas: Hardback, 190 pages, aukštis x plotis: 254x178 mm, weight: 535 g, 4 Tables, color; 30 Tables, black and white; 14 Line drawings, color; 1 Line drawings, black and white; 2 Halftones, color; 15 Illustrations, color; 2 Illustrations, black and white
  • Išleidimo metai: 30-Dec-2022
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1032232188
  • ISBN-13: 9781032232188
Kitos knygos pagal šią temą:
Knowledge Integration Methods for Probabilistic Knowledge-based SystemsDidesnis paveikslėlis
  • Formatas: Hardback, 190 pages, aukštis x plotis: 254x178 mm, weight: 535 g, 4 Tables, color; 30 Tables, black and white; 14 Line drawings, color; 1 Line drawings, black and white; 2 Halftones, color; 15 Illustrations, color; 2 Illustrations, black and white
  • Išleidimo metai: 30-Dec-2022
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1032232188
  • ISBN-13: 9781032232188
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781032232188.html
Raktažodžiai:
"Knowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. This book provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. It also provides the mathematical background to solve problems of restoring consistency and problems of integrating probabilistic knowledge bases in the integrating process. The research results presentedin the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more. Knowledge Integration Methods will be useful for Computer Science graduates and Ph.D students, in addition to researchers and readers working on knowledge management and ontology interpretation"--

This book provides a snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency and integrating knowledge bases. It provides the mathematical background to solve problems of restoring consistency and problems of integrating probabilistic knowledge bases.



Knowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. This book provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. It also provides the mathematical background to solve problems of restoring consistency and problems of integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more. Knowledge Integration Methods will be useful for Computer Science graduates and Ph.D students, in addition to researchers and readers working on knowledge management and ontology interpretation.
Preface ix
Authors xi
Chapter 1 Introduction
1(7)
1.1 Motivation
1(4)
1.2 The Objectives Of This Book
5(1)
1.3 The Structure Of This Book
6(2)
Chapter 2 Probabilistic knowledge-based systems
8(15)
2.1 Knowledge Base Representation
8(6)
2.1.1 Knowledge Representation Methods
8(2)
2.1.2 Probabilistic Knowledge Base Representation
10(4)
2.2 Types Of Knowledge-Based Systems
14(2)
2.3 The Knowledge-Based System Development
16(1)
2.4 Components Of A Probabilistic Knowledge-Based System
17(2)
2.5 Comparing Probabilistic Knowledge-Based System With Other Systems
19(3)
2.6 Concluding Remarks
22(1)
Chapter 3 Inconsistency measures for probabilistic knowledge bases
23(35)
3.1 Overview Of Inconsistency Measures
23(4)
3.1.1 Distance Functions
23(1)
3.1.2 Development of Inconsistency Measures
24(3)
3.2 Representing The Inconsistency Of The Probabilistic Knowledge Base
27(6)
3.2.1 Basic Notions
27(2)
3.2.2 Characteristic Model
29(2)
3.2.3 Desired Properties of Inconsistency Measures
31(2)
3.3 Inconsistency Measures For Probabilistic Knowledge Bases
33(16)
3.3.1 The Basic Inconsistency Measures
33(7)
3.3.2 The Norm-based Inconsistency Measures
40(5)
3.3.3 The Unnormalized Inconsistency Measure
45(4)
3.4 Algorithms For Computing The Inconsistency Measures
49(8)
3.4.1 The Computational Complexity
49(1)
3.4.2 The General Methods
50(1)
3.4.3 Algorithms
51(6)
3.5 Concluding Remarks
57(1)
Chapter 4 Methods for restoring consistency in probabilistic knowledge bases
58(33)
4.1 Overview Of Handling Inconsistencies
58(5)
4.1.1 The Inconsistency Resolution Problem
58(2)
4.1.2 Methods of Handling Inconsistencies
60(3)
4.2 Restoring Consistency In Probabilistic Knowledge Bases
63(4)
4.2.1 Basic Notions
63(1)
4.2.2 Desired Properties of Consistency-Restoring Operator
64(2)
4.2.3 A General Model for Restoring Consistency
66(1)
4.3 Methods For Restoring Consistency
67(17)
4.3.1 The Norm-based Consistency-restoring Problem
67(10)
4.3.2 The Unnormalized Consistency-Restoring Problem
77(7)
4.4 Algorithms For Restoring Consistency
84(6)
4.5 Concluding Remarks
90(1)
Chapter 5 Distance-based methods for integrating probabilistic knowledge bases
91(41)
5.1 Overview Of Knowledge Integration Methods
91(7)
5.1.1 The Knowledge Integration Problem
91(3)
5.1.2 Methods for Integrating Knowledge Bases
94(4)
5.2 Probabilistic Knowledge Integration
98(12)
5.2.1 Divergence Functions
98(4)
5.2.2 Distance-based Model for Integrating Probabilistic Knowledge Bases
102(2)
5.2.3 Desired Properties of Distance-based Probabilistic Integrating Operator
104(2)
5.2.4 Finding the Satisfying Probability Vector
106(4)
5.3 The Problems With Distance-Based Integrating Probabilistic Knowledge Bases
110(2)
5.4 Distance-Based Integrating Operators
112(15)
5.4.1 The Class of Probabilistic Integrating Operators
112(3)
5.4.2 The Class of Probabilistic Integrating Operators rHU
115(12)
5.5 Integration Algorithms
127(4)
5.5.1 Algorithm for Finding the Satisfying Probability Vector
127(1)
5.5.2 The Distance-based Integration Algorithm
128(2)
5.5.3 The HULL Algorithm
130(1)
5.6 Concluding Remarks
131(1)
Chapter 6 Value-based method for integrating probabilistic knowledge bases
132(15)
6.1 Value-Based Probabilistic Knowledge Integration
132(6)
6.1.1 Basic Notions
132(4)
6.1.2 Value-based Model for Integrating Probabilistic Knowledge Bases
136(1)
6.1.3 Desired Properties of Value-based Probabilistic Integrating Operator
137(1)
6.2 The Probability Value-Based Integrating Operators
138(3)
6.3 The Probability Value-Based Integration Algorithms
141(5)
6.3.1 Algorithm for Deducting Probabilistic Constraints
141(2)
6.3.2 Probability Value-based Integration Algorithms
143(3)
6.4 Concluding Remarks
146(1)
Chapter 7 Experiments and Applications
147(21)
7.1 Experiment
147(15)
7.1.1 Experimental Purpose and Assumptions
147(2)
7.1.2 Experiment Settings
149(2)
7.1.3 Experimental Implementation
151(1)
7.1.4 Results and Analysis
152(10)
7.2 Applications
162(6)
7.2.1 Artificial Intelligence and Machine Learning
162(1)
7.2.1.1 Machine Learning
162(2)
7.2.1.2 Recommendation Systems
164(1)
7.2.1.3 Group Decision-making
165(1)
7.2.2 Knowledge Systems
165(1)
7.2.3 Software Engineering
166(1)
7.2.4 Other Applications
167(1)
Chapter 8 Conclusions and open problems
168(3)
8.1 Conclusions
168(2)
8.2 Open Problems
170(1)
Bibliography 171(15)
Index 186
Van Tham Nguyen is currently PhD at Thuyloi University, Hanoi, Vietnam. He received his Ph.D. degree from VNU University of Engineering and Technology in 2022. His scientific interests consist of collective intelligence, knowledge integration methods, inconsistent knowledge processing, and machine learning. He has published nine peer-reviewed papers in journals and conference proceedings.

Ngoc Thanh Nguyen (Ph.D., D.Sc.) is a full professor and the Head of Applied Informatics Department at the Wroclaw University of Science and Technology, Poland. He is author or co-author of 6 books and over 450 papers.

Trong Hieu Tran is currently an Associate Professor at VNU University of Engineering and Technology. He received his dual Ph.D. degree from Wroclaw University of Technology (Poland) and Swinburne University of Technology (Australia) in 2013. His research interests include belief merging, multi-agent systems, data mining and machine learning. He is the author of 21 peer-reviewed papers in reputable journals and conferences.