Please wait a minute...
Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (3): 22-29    DOI: 10.11925/infotech.2096-3467.2017.1012
Current Issue | Archive | Adv Search |
Measuring Novelty of Scholarly Articles
Lu Wanhui1,2,3, Tan Zongying1,2()
1(National Science Library, Chinese Academy of Sciences, Beijing 100190, China)
2(University of Chinese Academy of Sciences, Beijing 100049, China)
3(Chinese Academy of Social Science Evaluation Studies, Chinese Academy of Social Sciences, Beijing 100732, China)
Download: PDF (952 KB)   HTML ( 3
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper aims to construct a novelty index to evaluate the academic achievements. [Methods] First, we proposed a model to calculate content eigenfactor based on deep learning (Doc2Vec) and Hidden Markov Model. Then, we built the topic novelty measure index. Finally, we examined the proposed method with academic papers published by three Chinese LIS journals in 2014. [Results] Compared with the existing methods, the proposed model measured the topic novelty more effectively. [Limitations] Our empirical research only examined abstracts of the academic papers. [Conclusions] The proposed method could help us evaluate and monitor scholarly research.

Key wordsNovelty of Topics      Content Eigenfactor      Academic Achievements     
Received: 09 October 2017      Published: 03 April 2018
ZTFLH:  G250  

Cite this article:

Lu Wanhui,Tan Zongying. Measuring Novelty of Scholarly Articles. Data Analysis and Knowledge Discovery, 2018, 2(3): 22-29.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1012     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I3/22

ID 文章题目 文章来源 主题新颖度 文献[18](α=0.5)
1 我国综合性文献数据库大学生用户心智模型结构测量实证研究 情报学报 0.4848 1
2 生态产业集群内知识转移影响因素的分析 情报科学 0.4836 0.6065
3 论中国公安情报学学科专业发展及研究框架 情报杂志 0.4812 0.3019
4 国内外网络舆情数学建模研究综述 情报杂志 0.4801 0.6065
5 数字鸿沟分析视角下的电子政务在线服务测评——基于北京市的实证研究 情报科学 0.4779 0.6065
6 知识网络的知识完备性测度方法研究 情报学报 0.4775 0.3679
7 欧美国家公共信息资源定价策略的发展演变分析 情报学报 0.4755 0.1353
8 基于仿生学视角的科技型新创企业知识转化影响因素研究 情报科学 0.4748 0.6065
9 应用“h2指数”评价高校档案馆学术能力的效果研究——以我国高校排名
前20名的高校档案馆为例
情报科学 0.4741 0.6065
10 复杂项目危机预警网格系统设计与实现 情报杂志 0.4723 0.4978
[1] 张京辉, 胡淑礼, 王亚非, 等. 软科学成果查新研究[J]. 软科学, 1993, 7(4): 28-33.
[1] (Zhang Jinghui, Hu Shuli, Wang Yafei, et al.Research on the Novelty Search of Soft Science[J]. Soft Science, 1993, 7(4): 28-33.)
[2] 《科技查新教程》编写组. 科技查新教程[M]. 北京: 机械工业出版社, 2001: 325.
[2] (Writing Group of Sci-Tech Novelty Retrieval Tutorial. Sci-Tech Novelty Retrieval Tutorial [M]. Beijing: China Machine Press, 2001: 325.)
[3] Spier R.The History of the Peer-Review Process[J]. Trends in Biotechnology, 2002, 20(8): 357-358.
doi: 10.1016/S0167-7799(02)01985-6 pmid: 12127284
[4] 杨京. 基于研究水平的单篇学术论文创新力评价研究[D]. 淄博: 山东理工大学, 2016.
[4] (Yang Jing.Evaluation of Paper’s Innovation Based on the Research Level [D]. Zibo: Shandong University of Technology, 2016.)
[5] 杨锋, 梁樑, 苟清龙, 等. 同行评议制度缺陷的根源及完善机制[J]. 科学学研究, 2008, 26(3): 569-572.
[5] (Yang Feng, Liang Liang, Gou Qinglong, et al.Origination and Improvement for the Pitfalls of Peer Review Systems[J]. Studies in Science of Science, 2008, 26(3): 569-572.)
[6] 沈律. 科技创新的一般均衡理论——关于科技成果创新度评价的科学计量学分析[J]. 科学学研究, 2003, 21(2): 205-209.
[6] (Shen Lv.A General Equilibrium Theory of the Science & Technology Innovation: On Scientometrics Analysis of the Science & Technology Innovation[J]. Studies in Science of Science, 2003, 21(2): 205-209.)
[7] 朱大明. 参考文献的主要作用与学术论文的创新性评审[J]. 编辑学报, 2004, 16(2): 91-92.
doi: 10.3969/j.issn.1001-4314.2004.02.006
[7] (Zhu Daming.Main Roles of References and Appraisal of Innovation of Academic Papers[J]. Acta Editologica, 2004, 16(2): 91-92.)
doi: 10.3969/j.issn.1001-4314.2004.02.006
[8] 祁延莉, 窦曦骞, 夏汇川. SCI量化评价功能的局限性及其修正[J]. 情报理论与实践, 2009, 32(9): 48-52.
[8] (Qi Yanli, Dou Xiqian, Xia Huichuan.Limitations of SCI Quantitative Evaluation Function & Its Modifications[J]. Information Studies: Theory & Application, 2009, 32(9): 48-52.
[9] Seglen P O.Why the Impact Factor of Journals Should Not Be Used for Evaluating Research[J]. BMJ, 1997, 314(7079): 498-502.
doi: 10.1136/bmj.314.7079.498 pmid: 9056805
[10] Leydesdorff L, Bornmann L, Comins J, et al. Citations: Indicators of Quality? The Impact Fallacy[J]. Frontiers in Research Metrics and Analysis, 2016, 1: Article 1.
[11] 尚海茹, 冯长根, 孙良. 用学术影响力评价学术论文——兼论关于学术传承效应和长期引用的两个新指标[J]. 科学通报, 2016, 61(26): 2853-2860.
[11] (Shang Hairu, Feng Changgen, Sun Liang.Evaluation of Academic Papers with Academic Influence — Proposing Two New Indicators of Academic Inheritance Effect and Long-term Citation[J]. Chinese Science Bulletin, 2016, 61(26): 2853-2860.)
[12] 吴勤. 基于引证强度的学术论文质量评价方法研究[J]. 情报学报, 2007, 26(4): 522-526.
doi: 10.3969/j.issn.1000-0135.2007.04.007
[12] (Wu Qin.Research on Quality Evaluation in the Academic Articles Based on the Intensity of Citation[J]. Journal of the China Society for Scientific and Technical Information, 2007, 26(4): 522-526.)
doi: 10.3969/j.issn.1000-0135.2007.04.007
[13] Hirsch J E.An Index to Quantify an Individual’s Scientific Research Output[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(46): 16569-16572.
doi: 10.1073/pnas.0507655102 pmid: 16275915
[14] 沈阳. 一种基于关键词的创新度评价方法[J]. 情报理论与实践, 2007, 30(1): 125-127.
[14] (Shen Yang.An Innovative Evaluation Method Based on Keywords[J]. Information Studies: Theory & Application, 2007, 30(1): 125-127.)
[15] 钱玲飞, 杨建林, 张莉. 基于关键词分析的学科创新力比较——以情报学图书馆学为例[J]. 情报理论与实践, 2011, 34(1): 117-120.
[15] (Qian Lingfei, Yang Jianlin, Zhang Li.Comparison of the Discipline Innovation Based on Keyword Analysis — Take the Information Science and Library Sciences as an Example[J]. Information Studies: Theory & Application, 2011, 34(1): 117-120.)
[16] 杨建林, 钱玲飞. 基于关键词对逆文档频率的主题新颖度度量方法[J]. 情报理论与实践, 2013, 36(3): 99-102.
[16] (Yang Jianlin, Qian Lingfei.A Novel Measurement Method of the Theme Based on Inverse Document Frequency of Keywords[J]. Information Studies: Theory & Application, 2013, 36(3): 99-102.)
[17] Mase H, Matsubayashi T, Ogawa Y, et al.Proposal of Two-stage Patent Retrieval Method Considering the Claim Structure[J]. ACM Transactions on Asian Language Information Processing, 2005, 4(2): 190-206.
doi: 10.1145/1105696.1105702
[18] 刘玉琴, 朱东华, 吕琳. 基于文本挖掘技术的产品技术成熟度预测[J]. 计算机集成制造系统, 2008, 14(3): 506-510.
doi: 10.1016/j.commatsci.2008.03.016
[18] (Liu Yuqin, Zhu Donghua, Lv Lin.Technology Maturity of Product Forecasting Based on Text Mining[J]. Computer Integrated Manufacturing Systems, 2008, 14(3): 506-510.)
doi: 10.1016/j.commatsci.2008.03.016
[19] Meng L, Huang R, Gu J.A Review of Semantic Similarity Measures in WordNet[J]. International Journal of Hybrid Information Technology, 2013, 6(1): 1-12.
[20] 孙润志. 基于语义理解的文本相似度计算研究与实现[D]. 北京: 中国科学院大学, 2015.
[20] (Sun Runzhi.Research and Implementation of Text Similarity Computing Based on Semantic Understanding[D]. Beijing: University of Chinese Academy of Sciences, 2015.)
[21] Word2Vec [EB/OL]. [2017-12-05]..
[22] Le Q V, Mikolov T.Distributed Representations of Sentences and Documents [OL]. arXiv Preprint, arXiv: 1405.405302.
[23] Lee S, Jin X, Kim W.Sentiment Classification for Unlabeled Dataset Using Doc2Vec with JST[C]// Proceedings of the 18th Annual International Conference on Electronic Commerce: E-Commerce in Smart Connected World. ACM, 2016: 28.
[24] Maslova N, Potapov V.Neural Network Doc2vec in Automated Sentiment Analysis for Short Informal Texts[A]// Lecture Notes in Computer Science[M]. Springer, 2017: 546-554.
[25] 逯万辉. 基于深度学习的学术期刊选题同质化测度方法研究[J]. 情报资料工作, 2017, 38(5): 105-112.
doi: 10.3969/j.issn.1002-0314.2017.05.016
[25] (Lu Wanhui.Research on Measuring Academic Journals Topics Homogenization Based on Deep Learning[J]. Information and Documentation Services, 2017, 38(5): 105-112.)
doi: 10.3969/j.issn.1002-0314.2017.05.016
[26] Sheldon D.Manipulation of Pagerank and Collective Hidden Markov Models[D]. Cornell University, 2010.
[27] 荆林波. 中国人文社会科学期刊评价报告[M]. 北京: 中国社会科学出版社, 2015.
[27] (Jing Linbo.Evaluation Report of Chinese Humanities and Social Sciences Journals [M]. Beijing: China Social Sciences Press, 2015.)
[1] Wang Sili, Zhu Zhongming, Yang Heng, Liu Wei. Research on Automatic Identification of Hypernym-Hyponym Relations of Domain Concepts Based on Pattern and Projection Learning [J]. 数据分析与知识发现, 0, (): 1-.
[2] Chenglei Qin, Chengzhi Zhang. Using Hierarchical Attention Network Model to Recognize Structure Functions of Academic Articles [J]. 数据分析与知识发现, 0, (): 1-.
[3] Xu Yicong,Tian Xuedong,Li Xinfu,Yang Fang,Shi Qingxuan. Retrieving Mathematical Expressions Based on Hesitant Fuzzy Weight[J]. 数据分析与知识发现, 2020, 4(7): 118-126.
[4] Liang Ye,Li Xiaoyuan,Xu Hang,Hu Yiran. CLOpin: A Cross-Lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning[J]. 数据分析与知识发现, 2020, 4(6): 1-14.
[5] Liu Weijiang,Wei Hai,Yun Tianhe. Evaluation Model for Customer Credits Based on Convolutional Neural Network[J]. 数据分析与知识发现, 2020, 4(6): 80-90.
[6] Qian Liu, Chenliang Li. A Survey of Topic Evolution on Social Media [J]. 数据分析与知识发现, 0, (): 1-.
[7] Shen Zhe, Wang Yi, Yao Yifan, Cheng Ying. Author Name Disambiguation Techniques for the Academic Literature: a Review [J]. 数据分析与知识发现, 0, (): 1-.
[8] Liang Ye, Li Xiaoyuan, Xu Hang, Hu Yiran. CLOpin: A Cross-lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning [J]. 数据分析与知识发现, 0, (): 1-.
[9] Hao Xu,Xuefang Zhu,Chengzhi Zhang,Chuan Jiang. System Analysis and Design for Methodological Entities Extraction in Full Text of Academic Literature[J]. 数据分析与知识发现, 2019, 3(10): 29-36.
[10] Li Dong,Tong Shouchuan,Li Jiang. Analyzing Interdisciplinarity and Scientists’ Academic Impacts[J]. 数据分析与知识发现, 2018, 2(12): 1-11.
[11] Yu Yan,Zhao Naixuan. Choosing Stopwords for Patent Topic Analysis Based on Auxiliary Set[J]. 数据分析与知识发现, 2018, 2(11): 95-103.
[12] Zhang Pengyi,Wang Danxue,Jiao Yifan,Chen Xiuyu,Wang Jun. Predicting Mobile Purchase Decisions Based on User Browsing Logs[J]. 数据分析与知识发现, 2018, 2(1): 51-63.
[13] Guo Shaoqing,Le Xiaoqiu. Identifying Actual Value of Numerical Indicator from Scientific Paper[J]. 数据分析与知识发现, 2018, 2(1): 21-28.
[14] Chen Guo,Xiao Lu. Linking Knowledge Elements from Online Community[J]. 数据分析与知识发现, 2017, 1(11): 75-83.
[15] Li Hui,Chai Yaqing. Analyzing Sentiment Polarity of Comments Based on Attributes[J]. 数据分析与知识发现, 2017, 1(10): 1-11.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn