Please wait a minute...
Advanced Search
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
全文: PDF (673 KB)   HTML ( 17
输出: BibTeX | EndNote (RIS)      
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。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
许鹏程
毕强
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
[1] 郭秋萍, 华康民 . 超网络研究分析综述[J]. 管理工程师, 2016,21(4):51-55.
[1] ( Guo Qiuping, Hua Kangmin . A Review of Research on Supernetwork[J]. Management Engineer, 2016,21(4):51-55.)
[2] Denning P J . The Science of Computing-Supernetworks[J]. American Scientist, 1985,73(3):225-227.
doi: 10.1055/a-1044-2397 pmid: 31842246
[3] Nagurney A, Dong J. Supernetworks: Decision-Making for the Information Age[M]. Cheltenham: Edward Elgar Publishing, 2002.
[4] Dong J, Nagurney A . A Supernetwork Model for Commuting Versus Telecommuting Decision Making[A]// Transportation Planning and Management in the 21st Century[M]. 2001: 386.
[5] 王志平, 王众托 . 超网络理论及其应用[M]. 北京: 科学出版社, 2008.
[5] ( Wang Zhiping, Wang Zhongtuo. Hypernetwork Theory and Application[M]. Beijing: Science Press, 2008.)
[6] Hearn G, Scott D . Students Staying Home: Questioning the Wisdom of a Digital Future for Australian Universities[J]. Futures, 1998,30(7):731-737.
doi: 10.1016/S0016-3287(98)00080-9
[7] 于洋 . 组织知识管理中的知识超网络研究[D]. 大连: 大连理工大学, 2009.
[7] ( Yu Yang . Researches on Knowledge Supernetwork in Organizational Knowledge Management[D]. Dalian: Dalian University of Technology, 2009.)
[8] Yu Y, Dang Y, Xu P , et al. Knowledge Resources Integrated Model of Basic Scientific Research Achievements Based on Supernetwork [C]// Proceedings of the 2008 International Seminar on Business and Information Management. 2008: 505-508.
[9] Chen T, Shao Y, Han Y . Collaborative Innovation Model Research Based on Knowledge-Supernetwork and TRIZ [C]// Proceedings of the 4th International Conference on Logistics, Informatics and Service Science. 2015: 1169-1174.
[10] 康阳春, 王海南 . 基于知识超网络的知识服务体系研究[J]. 图书情报工作, 2018,62(S1):64-67.
[10] ( Kang Yangchun, Wang Hainan . Research on Knowledge Service System Based on Knowledge Hypernetwork[J]. Library and Information Service, 2018,62(S1):64-67.)
[11] 田儒雅, 孙巍, 吴蕾 , 等. 基于超网络的图书情报领域知识合作特征分析[J]. 情报理论与实践, 2016,39(10):25-30.
[11] ( Tian Ruya, Sun Wei, Wu Lei , et al. Feature Analysis of Knowledge Cooperation in the Field of Library and Information Science Based on Supernetwork[J]. Information Studies: Theory & Application, 2016,39(10):25-30.)
[12] 肖璐 . 基于知识超网络的网络社区学术资源多粒度聚合研究[J]. 情报杂志, 2018,37(12):182-187, 194.
[12] ( Xiao Lu . Research on the Multi-Granularity Integration for Academic Resource in Network Community Based on the Knowledge Supernetwork[J]. Journal of Intelligence, 2018,37(12):182-187, 194.)
[13] Zhao L, Zhang H, Wu W . Cooperative Knowledge Creation in an Uncertain Network Environment Based on a Dynamic Knowledge Supernetwork[J]. Scientometrics, 2019,119(2):657-685.
doi: 10.1007/s11192-019-03049-4
[14] 李纲, 巴志超 . 科研合作超网络下的知识扩散演化模型研究[J]. 情报学报, 2017,36(3):274-284.
[14] ( Li Gang, Ba Zhichao . Research on Evolutionary Dynamics of Knowledge Diffusion Based on Collaboration Hypernetwork[J]. Journal of the China Society for Scientific and Technical Information, 2017,36(3):274-284.)
[15] 廖开际, 杨彬彬 . 基于加权超网络模型的组织知识共享研究[J]. 情报学报, 2013,32(5):503-510.
[15] ( Liao Kaiji, Yang Binbin . Research on Organizational Knowledge Sharing Based on the Weighted Supernetwork Model[J]. Journal of the China Society for Scientific and Technical Information, 2013,32(5):503-510.)
[16] Xia H, Wang Z, Luo S , et al. Toward a Concept of Community Intelligence: A View on Knowledge Sharing and Fusion in Web-Mediated Communities [C]// Proceedings of the 2008 IEEE International Conference on Systems, Man and Cybernetics. 2008: 88-93.
[17] 陆伟, 刘杰, 秦喜艳 . 基于专长词表的图情领域专家检索与评价[J]. 中国图书馆学报, 2010,36(2):70-76.
[17] ( Lu Wei, Liu Jie, Qin Xiyan . Expert Search and Evaluation Based on Expertise Vocabulary in the Field of Library and Information Science[J]. Journal of Library Science in China, 2010,36(2):70-76.)
[18] 胡月红, 刘萍 . 基于本体概念的专长表示研究[J]. 图书情报工作, 2012,56(4):17-21.
[18] ( Hu Yuehong, Liu Ping . An Ontology Based Approach for Expertise Representation[J]. Library and Information Service, 2012,56(4):17-21.)
[19] Bornmann L, Daniel H D . Does the h-index for Ranking of Scientists Really Work?[J]. Scientometrics, 2005,65(3):391-392.
doi: 10.1007/s11192-005-0281-4
[20] 邱均平, 缪雯婷 . h指数在人才评价中的应用——以图书情报学领域中国学者为例[J]. 科学观察, 2007(3):17-22.
[20] ( Qiu Junping, Miao Wenting . Application of h-index in Evaluating Individual’s Performances[J]. Science Focus, 2007(3):17-22.)
[21] 李江, 李东, 冯培桦 , 等. 基于专长吻合度、学术影响力与社会关联值的专家推荐模型研究[J]. 情报学报, 2017,36(4):338-345.
[21] ( Li Jiang, Li Dong, Feng Peihua , et al. An Expert Recommendation Model Based on the Speciality, Scientific Impact of Experts, and Social Relationship Between Experts and Applicants[J]. Journal of the China Society for Scientific and Technical Information, 2017,36(4):338-345.)
[22] Gu G, Deng W . Identification and Evaluation Methods of Expert Knowledge Based on Social Network Analysis [C]// Proceedings of the 2011 MSEC International Conference on Multimedia, Software Engineering and Computing. 2011: 219-225.
[23] Garces E, Anthony J . Identification of Experts Using Social Network Analysis(SNA) [C]// Proceedings of the 2016 Portland International Conference on Management of Engineering & Technology. IEEE, 2016: 1882-1896.
[24] 王菲菲, 王筱涵, 刘扬 . 三维引文关联融合视角下的学者学术影响力评价研究——以基因编辑领域为例[J]. 情报学报, 2018,37(6):610-620.
[24] ( Wang Feifei, Wang Xiaohan, Liu Yang . Evaluation of Scholarly Impact from an Integrated Perspective of Three-Dimensional Citations: A Case Study of Gene Editing[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(6):610-620.)
[25] Kongthon A, Haruechaiyasak C, Thaiprayoon S . Expert Identification for Multidisciplinary R&D Project Collaboration [C]// Proceedings of the 2009 Portland International Conference on Management of Engineering & Technology. IEEE, 2009: 1474-1480.
[26] 张晓娟, 陆伟, 程齐凯 . PLSA在图情领域专家专长识别中的应用[J]. 现代图书情报技术, 2012(2):76-81.
[26] ( Zhang Xiaojuan, Lu Wei, Cheng Qikai . Application of PLSA on Expertise Identifying in the Field of Library and Information Science[J]. New Technology of Library and Information Service, 2012(2):76-81.)
[27] 梁晓贺, 田儒雅, 吴蕾 , 等. 基于超网络的微博舆情主题挖掘方法[J]. 情报理论与实践, 2017,40(10):100-105.
[27] ( Liang Xiaohe, Tian Ruya, Wu Lei , et al. A Method of Public Opinion Topic Mining in Micro-blog Based on Super-network[J]. Information Studies: Theory & Application, 2017,40(10):100-105.)
[28] 马宁, 刘怡君 . 基于超网络中超边排序算法的网络舆论领袖识别[J]. 系统工程, 2013,31(9):1-10.
[28] ( Ma Ning, Liu Yijun . Recognition of Online Opinion Leaders Based on SuperEdgeRank Algorithm of Supernetwork[J]. Systems Engineering, 2013,31(9):1-10.)
[29] 王鹏, 高铖, 陈晓美 . 基于LDA模型的文本聚类研究[J]. 情报科学, 2015,33(1):63-68.
[29] ( Wang Peng, Gao Cheng, Chen Xiaomei . Research on LDA Model Based on Text Clustering[J]. Information Science, 2015,33(1):63-68.)
[30] 张磊, 马静, 李丹丹 , 等. 语义社会网络的超网络模型构建及关键节点自动化识别方法研究[J]. 现代图书情报技术, 2016(3):8-17.
[30] ( Zhang Lei, Ma Jing, Li Dandan , et al. Hypernetwork Model for Semantic Social Network and Automatic Identification of Key Nodes[J]. New Technology of Library and Information Service, 2016(3):8-17.)
[1] 谭学清,张磊,黄翠翠,罗琳. 融合领域专家信任与相似度的协同过滤推荐算法研究*[J]. 现代图书情报技术, 2016, 32(7-8): 101-109.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn