
Knowledge Integration Methods for Probabilistic Knowledge-based Systems
- 189 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Knowledge Integration Methods for Probabilistic Knowledge-based Systems
About this book
Knowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. Knowledge Integration Methods provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. The book also provides the mathematical background to solving problems of restoring consistency and 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. This text will be useful for computer science graduates and PhD students, in addition to researchers and readers working on knowledge management and ontology interpretation.
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Information
Table of contents
- Cover Page
- Half-Title Page
- Title Page
- Copyright Page
- Contents
- Preface
- Authors
- Chapter 1 ▪ Introduction
- Chapter 2 ▪ Probabilistic knowledge-based systems
- Chapter 3 ▪ Inconsistency measures for probabilistic knowledge bases
- Chapter 4 ▪ Methods for restoring consistency in probabilistic knowledge bases
- Chapter 5 ▪ Distance-based methods for integrating probabilistic knowledge bases
- Chapter 6 ▪ Value-based method for integrating probabilistic knowledge bases
- Chapter 7 ▪ Experiments and Applications
- Chapter 8 ▪ Conclusions and open problems
- Bibliography
- Index
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