Knowledge Integration Methods for Probabilistic Knowledge-based Systems
1st Edition
Published on 30. December 2022
202 pages
978-1-000-80999-2 (ISBN)
Description
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.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
College/higher education
Product notice
Reflowable
Illustrations
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
ISBN-13
978-1-000-80999-2 (9781000809992)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Van Tham Nguyen | Ngoc Thanh Nguyen | Trong Hieu Tran
Knowledge Integration Methods for Probabilistic Knowledge-based Systems
Book
10/2024
1st Edition
Chapman & Hall/CRC
€67.40
Shipment within 10-20 days
Van Tham Nguyen | Ngoc Thanh Nguyen | Trong Hieu Tran
Knowledge Integration Methods for Probabilistic Knowledge-based Systems
Book
12/2022
1st Edition
Chapman & Hall/CRC
€129.50
Shipment within 10-20 days
Persons
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.
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.
Content
1. Introduction 2. Probabilistic Knowledge-based Systems 3. Consistency Measures for Probabilistic Knowledge Bases 4. Methods for Restoring Consistency in Probabilistic Knowledge Bases 5. Distance-Based Methods for Integrating Probabilistic Knowledge Bases 6. Value-based Method for Integrating Probabilistic Knowledge Bases 7. Experiments and Applications 8. Conclusions and Open Problems
