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2023 , Vol. 67 >Issue 9: 12 - 22

DOI: https://doi.org/10.13266/j.issn.0252-3116.2023.09.002

SPECIAL TOPIC: Detection of Emerging Topics from the Perspective of Multiple Data Fusion

The Review of Quantification on Emerging Topic Attributes

  • Ding Jingda ,
  • Zhong Jianlan
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  • School of Cultural Heritage and Information Management, Shanghai University, Shanghai 200444

Received date: 2022-10-24

  Revised date: 2023-01-05

  Online published: 2023-05-11

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Abstract

[Purpose/Significance] By combing the research results of attribute quantification of emerging topics in recent ten years at home and abroad, it will provide reference for the improvement of the detection methods of emerging topics. [Method/Process] On the basis of combing the related concepts of emerging topics, the main attributes of emerging topics were summarized by using text content analysis, and the current research progress was summarized from two aspects: single attribute quantification and multi-attribute quantification. [Result/Conclusion] The current studies mainly quantify the novelty, growth, attentiveness, impact, coherence and persistence of emerging topics based on the external characteristics of literature, and multi-attribute quantization methods include weighting method, intersection method, complex fusion method and regression method. However, the existing quantification methods still have some shortcomings. In the future, it is necessary to improve the comprehensiveness and accuracy of attribute quantification based on multivariate data, explore the quantification methods of other attributes of emerging topics, verify the scientificity of threshold setting of attribute quantification results, strengthen the research on the importance of attributes of emerging topics and the correlation between attributes, and further broaden the research fields selected by attribute quantification methods of emerging topics.

Key words： emerging topics; attribute quantification; quantitative method

Cite this article

Ding Jingda , Zhong Jianlan . The Review of Quantification on Emerging Topic Attributes[J]. Library and Information Service, 2023 , 67(9) : 12 -22 . DOI: 10.13266/j.issn.0252-3116.2023.09.002

References

[1] XU S, HAO L, AN X, et al. Review on emerging research topics with key-route main path analysis[J]. Scientometrics, 2020, 122(1):607-624.
[2] 杨金庆, 魏雨晗, 黄圣智, 等. 基于科技文献的新兴主题识别研究综述[J]. 情报科学, 2020, 38(8):159-163, 177.
[3] 刘小玲, 谭宗颖. 新兴技术主题识别方法研究进展[J]. 图书情报工作, 2020, 64(11):145-152.
[4] 钟辉新. 新兴趋势探测研究综述[J]. 现代情报, 2017, 37(12):162-167.
[5] 卢超, 侯海燕, DING Y, 等. 国外新兴研究话题发现研究综述[J]. 情报学报, 2019, 38(1):97-110.
[6] 张仪. 形式概念分析与普通逻辑中的概念[D]. 镇江:江苏科技大学, 2017.
[7] 柴成鱼. 社会科学中的概念问题研究[D]. 太原:山西大学, 2010.
[8] MATSUMUR N, MATSUO Y, OHSAWA Y, et al. Discovering emerging topics from WWW[J]. Journal of contingencies and crisis management, 2002, 10(2):73-81.
[9] GLANZEL W, THIJS B. Using 'core documents' for detecting and labelling new emerging topics[J]. Scientometrics, 2012, 91(2):399-416.
[10] SMALL H, BOYACK K W, KLAVANS R. Identifying emerging topics in science and technology[J]. Research policy, 2014, 43(8):1450-1467.
[11] 唐月强. 基于文献的新兴主题识别研究[D]. 北京:北京工业大学, 2015.
[12] WANG Q. A bibliometric model for identifying emerging research topics[J]. Journal of the Association for Information Science and Technology, 2018, 69(2):290-304.
[13] 许海云, 王超, 岳增慧, 等. 基于知识网络强弱关系变迁测度新兴主题的未来不确定性[J]. 情报理论与实践, 2021, 44(11):89-98.
[14] TU Y, SENG J. Indices of novelty for emerging topic detection[J]. Information processing & management, 2012, 48(2):303-325.
[15] 徐路路, 王效岳, 白如江, 等. 基于DTM模型和文本特征分析的基金项目新兴趋势探测研究——以NSF石墨烯领域为例[J]. 数据分析与知识发现, 2018, 2(3):87-97.
[16] XU H, WINNINK J, YUE Z, et al. Multidimensional scientometric indicators for the detection of emerging research topics[J]. Technological forecasting and social change, 2021, 163:120490.
[17] YANG J, LU W, HU J, et al. A novel emerging topic detection method:a knowledge ecology perspective[J]. Information processing and management, 2022, 59(2):102843.
[18] XU S, HAO L Y, AN X, et al. Emerging research topics detection with multiple machine learning models[J]. Journal of informetrics, 2019, 13(4):100983.
[19] 杨金庆, 肖兵, 程秀峰, 等. 基于HDP过程模型与学术会议的学科新兴主题发现研究——以"人工智能"领域为例[J]. 情报理论与实践, 2019, 42(4):117-122.
[20] XU M, LI G, WANG X. Detecting emerging topics by exploiting probability burst and association rule mining:a case study of library and information science[J]. Malaysian journal of library & information science, 2020, 25(1):47-66.
[21] 白敬毅, 颜端武, 陈琼. 基于主题模型和曲线拟合的新兴主题趋势预测研究[J]. 情报理论与实践, 2020, 43(7):130-136, 193.
[22] HUANG L, CHEN X, NI X, et al. Tracking the dynamics of coword networks for emerging topic identification[J]. Technological forecasting and social change, 2021, 170:120944.
[23] LIANG Z, MAO J, LU K, et al. Combining deep neural network and bibliometric indicator for emerging research topic prediction[J]. Information processing and management, 2021, 58(5):102611.
[23] YANG J, LU W, HU J, et al. A novel emerging topic detection method:a knowledge ecology perspective[J]. Information processing and management, 2022, 59(2):102843.
[24] PORTER A L, GARNER J, CARLEY S F, et al. Emergence scoring to identify frontier R&D topics and key players[J]. Technological forecasting and social change, 2019, 146:628-643.
[25] MUND C, NEUHAUSLER P. Towards an early-stage identification of emerging topics in science-the usability of bibliometric characteristics[J]. Journal of informetrics, 2015, 9(4):1018-1033.
[26] KWON S, YOUTIE J, PORTER A L. Interdisciplinary knowledge combinations and emerging technological topics:implications for reducing uncertainties in research evaluation[J]. Research evaluation, 2020, 30(1):127-140.
[27] 段庆锋, 潘小换. 利用社交媒体识别学科新兴主题研究[J]. 情报学报, 2017, 36(12):1216-1223.
[28] 宋欣娜, 郭颖, 席笑文. 基于专利文献的多指标新兴技术识别研究[J]. 情报杂志, 2020, 39(6):76-81, 88.
[29] 徐路路, 靳杨. 基于FSD模型的政府资助项目新兴主题探测与分析[J]. 科学学与科学技术管理, 2019, 40(2):40-54.
[30] 唐恒, 邱悦文. 多源信息视角下的多指标新兴技术主题识别研究——以智能网联汽车领域为例[J]. 情报杂志, 2021, 40(3):81-88.
[31] 范云满, 马建霞. 基于LDA与新兴主题特征分析的新兴主题探测研究[J]. 情报学报, 2014, 33(7):698-711.
[32] 叶光辉, 王灿灿, 李松烨. 基于SciTS会议文本的跨学科科研协作新兴主题识别及预测[J]. 情报科学, 2022, 40(7):126- 135.
[33] ZHANG S, HAN F. Identifying emerging topics in a technological domain[J]. Journal of intelligent & fuzzy systems applications in engineering, 2016, 31(4):2147-2157.
[34] 黄璐, 朱一鹤, 张嶷. 基于加权网络链路预测的新兴技术主题识别研究[J]. 情报学报, 2019, 38(4):335-341.
[35] 陈稳, 陈伟. 基于计量指标多变量LSTM模型的新兴主题热度预测研究[J]. 数据分析与知识发现, 2022, 6(10):35-45.
[36] CHOI H, WOO J. Investigating emerging hydrogen technology topics and comparing national level technological focus:patent analysis using a structural topic model[J]. Applied energy, 2022, 313:118898.
[37] GONZALEZ-ALCAIDE G, LLORENTE P, RAMOS J. Bibliometric indicators to identify emerging research fields:publications on mass gatherings[J]. Scientometrics, 2016, 109(2):1283-1298.
[38] 黄鲁成, 张璐, 吴菲菲, 等. 基于突现文献和SAO相似度的新兴主题识别研究[J]. 科学学研究, 2016, 34(6):814-821.
[39] MEJIA C, KAJIKAWA Y. Emerging topics in energy storage based on a large-scale analysis of academic articles and patents[J]. Applied energy, 2020, 263:114625.
[40] 葛菲, 谭宗颖. 学科领域主题新兴趋势探测方法研究——基于关键词生命周期和引文分析[J]. 情报理论与实践, 2013, 36(9):78-82.
[41] OHNIWA R L, HIBINO A. Generating process of emerging topics in the life sciences[J]. Scientometrics, 2019, 121(3):1549- 1561.
[42] 刘自强, 胡正银, 许海云, 等. 基于PWLR模型的领域新兴趋势识别及其可视化研究[J]. 情报学报, 2020, 39(9):979-988.
[43] SASAKI H, FUGETSU B, SAKATA I. Emerging scientific field detection using citation networks and topic models-a case study of the nanocarbon field[J]. Applied system innovation, 2020, 3(3):1-17.
[44] 黄鲁成, 唐月强, 吴菲菲, 等. 基于文献多属性测度的新兴主题识别方法研究[J]. 科学学与科学技术管理, 2015, 36(2):34- 43.
[45] 俞立平, 张再杰, 肖成华. "以刊评文"的局限、本质及其辩证应用研究[J]. 情报杂志, 2021, 40(3):201-207.
[46] ROCHE I, BESAGNI D, FRANOIS C, et al. Identification and characterisation of technological topics in the field of Molecular Biology[J]. Scientometrics, 2010, 82(3):663-676.
[47] GONZALEZ-ALCAIDE G, GORRAIZ J, HERVAS-OLIVER J. On the use of bibliometric indicators for the analysis of emerging topics and their evolution:spin-offs as a case study[J]. Profesional de la informacion, 2018, 27(3):493-510.
[48] 卢超, 章成志, 王玉琢, 等. 语义特征分析的深化——学术文献的全文计量分析研究综述[J]. 中国图书馆学报, 2021, 47(2):110-131.
[49] HU Z, ZENG R, PENG L, et al. Discovering emerging research topics based on SPO predications[C]//14th international conference on knowledge management in organizations (KMO)-synergistic role of knowledge management in organization. Berlin:Springer, 2019, 1027:110-121.
[50] 徐路路, 王效岳, 白如江. 基于PLDA模型与多数据源融合相关性分析的新兴主题探测研究——以石墨烯领域为例[J]. 情报理论与实践, 2018, 41(4):63-69, 43.
[51] 段庆锋, 闫绪娴, 陈红, 等. 基于媒介比较的学科新兴主题动态识别-altmetrics与引文数据的融合方法[J]. 情报学报, 2022, 41(9):930-944.
[52] 牌艳欣, 李长玲, 刘运梅. 基于z指数的AAS高关注度学科研究主题识别[J]. 情报资料工作, 2019, 40(6):30-37.
[53] 段庆锋, 陈红, 刘东霞, 等. 基于LSTM模型与加权链路预测的学科新兴主题成长性识别研究[J]. 现代情报, 2022, 42(9):37-48, 142.
[54] 王鹏飞, 刘烜贞. 基于内容分析的Altmetrics本质研究[J]. 图书情报工作, 2017, 61(2):114-120.
[55] ROTOLO D, HICKS D, MARTIN B. What is an emerging technology?[J]. Research policy, 2015, 44(10):1827-1843.
[56] 郝雯柯, 杨建林. 基于语义表示和动态主题模型的社科领域新兴主题预测研究[J/OL]. 情报理论与实践:1-15[2022-10-16]. https://kns.cnki.net/kcms/detail/11.1762.G3.20220907.1612.002.html.
[57] XU S, HAO L, YANG G, et al. A topic models based framework for detecting and forecasting emerging technologies[J]. Technological forecasting & social change, 2021, 162:120366.
[58] ZHOU Y, DONG F, LIU Y, et al. A deep learning framework to early identify emerging technologies in large-scale outlier patents:an empirical study of CNC machine tool[J]. Scientometrics, 2021, 126(2):969-994.
[59] 许海云, 张慧玲, 武华维, 等. 新兴研究主题在演化路径上的关键时间点研究[J]. 图书情报工作, 2021, 65(8):51-64.
[60] CARLEY S F, NEWMAN N C, PORTER A L, et al. A measure of staying power:Is the persistence of emergent concepts more significantly influenced by technical domain or scale?[J]. Scientometrics, 2017, 111(3):2077-2087.
[61] 李静, 徐路路, 赵素君. 基于时间序列分析和SVM模型的基金项目新兴主题趋势预测与可视化研究[J]. 情报理论与实践, 2019, 42(1):118-123, 152.
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