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In [30], Chen Yang and his colleagues have used expert finding method for expert recommendation in online scientific communities.
In this study, a hybrid method for expert finding in online communities is presented which is based on content analysis and social network analysis.
Kudelka et al. [54] introduced a new soft computing method for expert identification in social networks based on formal concept analysis and fuzzy rules.
In future work, an experiment will be performed in a real environment by using an improved system to evaluate the effectiveness of the proposed method for expert or beginner operators.
In this study, a hybrid method for expert finding in online communities is presented; in our method content analysis is performed by analyzing concept map and social network analysis is based on PageRank algorithm.
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We do not attempt to describe all available methods for expert elicitation (for reviews the reader is referred to e.g. [ 25- 29]).
We devised a transparent, repeatable, and modifiable method for collecting expert opinion that describes and documents how threats affect marine ecosystems.
This paper helps in tuning and designing the membership functions that are best suited for the problem statement by integrating subtractive clustering method for fuzzy expert system design.
The literature endorses converging multiple techniques as the best method for eliciting expert knowledge in these real-world environments [14].
In [26], Bozzon and his colleagues have introduced a method for finding expert in social networks based on text analysis and social network context.
Interview studies aimed at eliciting these skills have frequently used the critical incident technique [ 13] as a method for examining expert performance relevant to safe care.
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