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Identifying Domain Experts Based on Knowledge Super-Network |
Pengcheng Xu(),Qiang Bi |
School of Management, Jilin University, Changchun 130022, China |
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Abstract [Objective] This paper evaluates the influence of scholars in a more scientific and standardized way, aiming to find domain experts effectively. [Methods] Firstly, we constructed a knowledge super-network model from four dimensions: author, literature, domain and subject. Secondly, we used the measurement methods for super-network and literature, the LDA model and the PageRank ranking algorithm, to present a domain expert identification method based on knowledge super-network. [Results] We used library and information science as the field to examine the proposed model and found it yielded better results than h-index, p-index and social network analysis. [Limitations] We only retrieved papers from some journals, which may affect the results with other data. The granularity of mining domain labels through the LDA topic model needs to be refined. [Conclusions] Based on the knowledge super-network of scientific and technological literature, the proposed method could assess the academic impacts effectively, and provides new ideas to identify domain experts.
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Received: 20 May 2019
Published: 18 December 2019
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Corresponding Authors:
Pengcheng Xu
E-mail: xupchup@protonmail.com
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