Overview
- Presents a novel approach to displaying and structuring knowledge with the aid of granular computing
- Introduces an expert finder system that employs an explicit and implicit knowledge profiling approach
- Demonstrates how big data tools and machine learning can be used to create granular, hierarchical knowledge structures
Part of the book series: Fuzzy Management Methods (FMM)
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Table of contents (9 chapters)
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Front Matter
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Motivation and Objectives
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Front Matter
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Background
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Front Matter
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Conceptual Framework
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Front Matter
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Architecture and Inference System
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Front Matter
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Conclusions
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Front Matter
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Back Matter
Keywords
About this book
This book introduces a novel type of expert finder system that can determine the knowledge that specific users within a community hold, using explicit and implicit data sources to do so. Further, it details how this is accomplished by combining granular computing, natural language processing and a set of metrics that it introduces to measure and compare candidates’ suitability. The book describes profiling techniques that can be used to assess knowledge requirements on the basis of a given problem statement or question, so as to ensure that only the most suitable candidates are recommended.
The book brings together findings from natural language processing, artificial intelligence and big data, which it subsequently applies to the context of expert finder systems. Accordingly, it will appeal to researchers, developers and innovators alike.Authors and Affiliations
Bibliographic Information
Book Title: Granular Knowledge Cube
Book Subtitle: An Expert Finder System for Knowledge Carriers
Authors: Alexander Denzler
Series Title: Fuzzy Management Methods
DOI: https://doi.org/10.1007/978-3-030-22978-8
Publisher: Springer Cham
eBook Packages: Business and Management, Business and Management (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-22977-1Published: 04 July 2019
Softcover ISBN: 978-3-030-22980-1Published: 14 August 2020
eBook ISBN: 978-3-030-22978-8Published: 17 June 2019
Series ISSN: 2196-4130
Series E-ISSN: 2196-4149
Edition Number: 1
Number of Pages: XIII, 164
Topics: Business Information Systems, Data Mining and Knowledge Discovery, Knowledge Management, Data Storage Representation, Artificial Intelligence, Computational Linguistics