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.
Table of Content
Part I: Motivation and Objectives.- Part II: Background.- Part III: Conceptual Framework.- Part IV: Architecture and Inference System.- Part V: Conclusions.
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