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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (11): 89-98    DOI: 10.11925/infotech.2096-3467.2019.0532
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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.

Key wordsKnowledge Super-Network      Domain Experts      SuperEdgeRank      Expert Identification     
Received: 20 May 2019      Published: 18 December 2019
ZTFLH:  TP391  
Corresponding Authors: Pengcheng Xu     E-mail: xupchup@protonmail.com

Cite this article:

Pengcheng Xu,Qiang Bi. Identifying Domain Experts Based on Knowledge Super-Network. Data Analysis and Knowledge Discovery, 2019, 3(11): 89-98.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0532     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I11/89

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... ... ... ... ...
SE36814 a14946 p15192 d23 k197, k14, k1764, k3056, k3454
SE36815 a6467 p15499 d23 k5595, k12103, k11647, k1189, k774
SE36816 a3770 p15192 d23 k5595, k12103, k11647, k1189, k774
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作者 研究领域 发文量 被引频次 篇均被引 h指数 排名 P指数 排名 中心度 排名 S值 排名
邱均平 文献计量; 情报学发展 147 5 579 37.95 37 1 59.60 2 179 1 0.002246 1
张晓林 数字图书馆; 图书馆发展 81 4 363 53.86 30 2 61.71 1 144 2 0.002198 2
初景利 图书馆发展 41 2 140 52.20 21 8 48.16 3 68 8 0.002081 3
蒋永福 图书馆学基础理论 37 1 776 48.00 25 3 44.01 4 22 152 0.001984 4
朱庆华 用户信息行为 91 2 217 24.36 23 4 37.80 5 131 11 0.001937 5
王子舟 图书馆学基础理论 28 974 34.78 18 12 32.35 6 25 65 0.001923 6
马费成 信息资源管理; 信息经济 61 1 332 21.83 22 5 30.75 8 61 12 0.001905 7
陈传夫 图书馆发展 27 939 34.78 17 19 31.96 7 25 137 0.001891 8
胡昌平 信息服务 50 1 155 23.10 21 6 29.88 11 41 83 0.001879 9
邓胜利 信息服务; 用户信息行为 47 1 153 24.53 19 11 30.47 10 33 84 0.001867 10
冷伏海 情报研究; 知识发现 71 1 427 20.10 18 12 30.61 9 86 23 0.001855 11
盛小平 知识管理; 图书馆建设 73 1 219 16.70 21 6 27.31 14 55 14 0.001832 12
赵蓉英 学术评价; 文献计量 57 1 162 20.38 18 12 28.71 13 77 13 0.001824 13
肖希明 信息共享; 资源整合 36 977 27.15 17 19 29.83 12 21 155 0.001816 14
黄晓斌 竞争情报 48 930 19.37 20 9 26.21 17 48 28 0.001803 15
苏新宁 情报学发展 67 1 114 16.62 18 12 26.45 16 105 5 0.001780 16
叶继元 学术评价 36 841 23.37 17 19 26.99 15 19 246 0.001771 17
毕 强 数字图书馆; 资源聚合 110 1 277 11.61 20 9 24.57 19 141 3 0.001759 18
王知津 情报学科发展; 竞争情报 64 966 15.09 18 12 24.44 20 80 25 0.001747 19
黄如花 开放获取 57 958 16.81 18 12 25.24 18 33 90 0.001732 20
h指数 p指数 Degree
S值 Pearson 0.725** 0.956** 0.185
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