Overview
- Applies fuzzy logic to represent and process uncertain semantic data with the Resource Description Framework (RDF)
- Presents recent advances in fuzzy RDF data modeling and management
- Explicitly describes the modeling and dealing with uncertainty in semantic data
Part of the book series: Studies in Computational Intelligence (SCI, volume 1057)
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Table of contents (5 chapters)
Keywords
About this book
This book systemically presents the latest research findings in fuzzy RDF data modeling and management. Fuzziness widely exist in many data and knowledge intensive applications. With the increasing amount of metadata available, efficient and scalable management of massive semantic data with uncertainty is of crucial importance. This book goes to great depth concerning the fast-growing topic of technologies and approaches of modeling and managing fuzzy metadata with Resource Description Framework (RDF) format. Its major topics include representation of fuzzy RDF data, fuzzy RDF graph matching, query of fuzzy RDF data, and persistence of fuzzy RDF data in diverse databases. The objective of the book is to provide the state-of-the-art information to researchers, practitioners, and postgraduates students who work on the area of big data intelligence and at the same time serve as the uncertain data and knowledge engineering professional as a valuable real-world reference.
Authors and Affiliations
Bibliographic Information
Book Title: Modeling and Management of Fuzzy Semantic RDF Data
Authors: Zongmin Ma, Guanfeng Li, Ruizhe Ma
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-031-11669-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-11668-1Published: 09 September 2022
Softcover ISBN: 978-3-031-11671-1Published: 10 September 2023
eBook ISBN: 978-3-031-11669-8Published: 08 September 2022
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XI, 210
Number of Illustrations: 35 b/w illustrations, 6 illustrations in colour
Topics: Computational Intelligence, Data Engineering, Artificial Intelligence