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数据分析与知识发现  2019, Vol. 3 Issue (11): 89-98     https://doi.org/10.11925/infotech.2096-3467.2019.0532
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于知识超网络的领域专家识别研究
许鹏程(),毕强
吉林大学管理学院 长春 130022
Identifying Domain Experts Based on Knowledge Super-Network
Pengcheng Xu(),Qiang Bi
School of Management, Jilin University, Changchun 130022, China
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摘要 

【目的】更加科学规范地对学者影响力进行评估, 从而发现领域专家。【方法】从作者、文献、领域、主题4个维度构建知识超网络模型; 结合超网络的度量方法、文献计量法, 运用LDA主题模型, 借鉴PageRank排序的思路, 提出基于知识超网络的领域专家识别方法。【结果】以图书情报领域为例, 通过实验进行领域专家识别, 并将结果与h指数、p指数、社会网络分析法进行对比, 验证了本文方法的有效性及合理性。【局限】只选取部分期刊的论文数据进行实验, 排序结果与真实的排序可能有差别; 通过LDA主题模型挖掘的领域标签的粒度需要进一步细化。【结论】基于科技文献的知识超网络, 探索学术影响力评价的科学范式, 为领域专家识别提供了新的思路和方法。

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许鹏程
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关键词 知识超网络领域专家超边排序专家识别    
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
收稿日期: 2019-05-20      出版日期: 2019-12-18
ZTFLH:  TP391  
通讯作者: 许鹏程     E-mail: xupchup@protonmail.com
引用本文:   
许鹏程,毕强. 基于知识超网络的领域专家识别研究[J]. 数据分析与知识发现, 2019, 3(11): 89-98.
Pengcheng Xu,Qiang Bi. Identifying Domain Experts Based on Knowledge Super-Network. Data Analysis and Knowledge Discovery, 2019, 3(11): 89-98.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0532      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I11/89
  基于知识超网络的领域专家识别框架
  基于知识超网咯的领域专家识别实验流程
超边SEi 作者ai 文献pi 领域di 主题词ki
SE1 a1 p1 d1 k1, k1224, k13342, k7779, k7467
SE2 a2 p1 d1 k2, k6851, k13343, k19220, k22757
SE3 a3 p1 d1 k3, k1971, k5790, k371, k2059
SE4 a2 p2 d1 k4, k6852, k10075, k8587, k1226
... ... ... ... ...
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
SE36817 a5858 p15192 d23 k5595, k12103, k11647, k1189, k774
  部分超边组成情况
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
  不同指标排序结果对比
h指数 p指数 Degree
S值 Pearson 0.725** 0.956** 0.185
  S值与h指数、p指数、Degree相关性
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