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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (4): 16-27    DOI: 10.11925/infotech.2096-3467.2021.0774
Current Issue | Archive | Adv Search |
Review of Studies Identifying Research Interests
Shi Xiang,Liu Ping()
School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper examines the significance, perspective, and related technical methods identifying scholars’ research interests, aiming to provide references for future studies. [Coverage] We used “scholar profile” and “research interest” as keywords to search CNKI, Web of Science and DBLP, which retrieved 62 representative articles. [Methods] We reviewed these studies from the perspectives of words, topics and networks. We also analyzed their developments and future trends. [Results] The research at word and topic levels were well-developed, which can effectively identify scholars’ research interests and their evolutionary characteristics. However, the research at the network level merit more attention. [Limitations] This paper did not thoroughly discussed technical details of the relevant algorithms. [Conclusions] More studies need to be conducted on scholars’ research interest association and semantic recognition, as well as their semantic descriptions.

Key wordsScholar Profile      Research Interest      Interest Model      Comprehensive Information Theory     
Received: 30 July 2021      Published: 12 May 2022
ZTFLH:  TP391  
Fund:National Natural Science Foundation of China(72174156)
Corresponding Authors: Liu Ping,ORCID:0000-0003-4695-3264     E-mail: pliuleeds@126.com

Cite this article:

Shi Xiang, Liu Ping. Review of Studies Identifying Research Interests. Data Analysis and Knowledge Discovery, 2022, 6(4): 16-27.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0774     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I4/16

模型 类型 时间 特点
LDA 静态主题模型 2003 生成式贝叶斯模型
AT 2004 LDA基础上引入学者变量
URI-ATM 2015 AT基础上改进识别非流行主题
ATT+LDA 2018 复合主题模型,同时考虑多个因素
JointAT 2020 AT基础上改进识别多语言主题
TAT 动态主题模型 2012 AT基础上引入时间变量,AT和ToT模型融合
AToT 2013 与TAT类似,仅在概率分布上存在不同
ATF 2016 TAT改进,每个学者对应不同的时间分布
Research Interest Topic Model
Research Interest Co-occurrence Network
Research Interest Ontology
研究维度 研究目的 研究问题 关键技术 局限性
词汇
(语法信息)
利用词汇模型帮助人们认识学者研究兴趣外在形态,简单地呈现学者研究兴趣演化的方式 ①学者有哪些研究兴趣
—词汇和权重识别
②学者研究兴趣的演化方式是怎样的
—词汇变化、词汇权重变化
①词频统计
—词频、TF-IDF
②语言模型
—语言模板、N-gram
③机器学习
—CRF
①词汇缺乏统一的规范,学者研究兴趣描述的准确性和全面性有待商榷
②词汇间相互独立,未考虑研究兴趣间的联系
主题
(语义信息)
将具有相同语义的词汇聚集在一起,更加精准地呈现学者的研究兴趣及其演化方式 ①如何将零散的词汇聚集在一起形成更为规范有效的描述
—主题识别
②如何解释学者研究兴趣的演化现象
—主题变化
①静态主题模型
—LDA、AT、JointAT、URI-ATM
②动态主题模型
—TAT、AToT、ATF
①主题仅将具有相似语义的词汇聚集在一起,并没有明确地揭示出词汇间的联系
②学者研究兴趣的识别仍停留在客观现象的解析上
网络
(语用信息)
利用网络模型将词汇之间进行关联,更深层次地刻画学者的研究兴趣及其演化特征,呈现出学者的知识结构 ①学者研究兴趣词汇间存在怎样的内在联系
—词汇关系识别
②学者研究兴趣的演化特征
—网络结构与语义特征分析
①共现网络
—词汇共现次数
②语义网络
—关系识别
①目前网络模型的语义表达能力较弱,无法体现研究兴趣之间的语义关联
②网络模型相较复杂,模型的分析和应用还存在很多空白
Comparison of Research Interest Recognition Methods
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