弱信号识别研究综述：寻找微弱的未来信号

doi:10.3772/j.issn.1000-0135.2023.08.010

情报学报

2023, Vol. 42

Issue (8): 996-1008 DOI: 10.3772/j.issn.1000-0135.2023.08.010

情报综述与述评

本期目录 | 过刊浏览 | 高级检索

弱信号识别研究综述：寻找微弱的未来信号

韩盟, 陈悦, 王玉奇, 宋凯, 王康

大连理工大学科学学与科技管理研究所暨WISE实验室，大连 116000

Searching for Weak Future Signals: A Review of Weak Signal Identification

Han Meng, Chen Yue, Wang Yuqi, Song Kai, Wang Kang

The Institute of Science of Science and S&T Management & WISE Lab, Dalian University of Technology, Dalian 116000

摘要
图/表
参考文献
相关文章 (1)

全文: PDF (885 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要作为提早预判未来而减弱不确定性的一种重要方式和手段，弱信号识别对于行为主体规避风险、把握战略主动具有重要的意义，现已受到学术界的广泛关注。对弱信号识别的相关文献进行综述，能够厘清研究现状、发现研究重点、认清研究方向，为相关科研人员提供参考。第一，梳理弱信号及相关概念的界定和发展脉络；第二，总结弱信号识别研究的相关理论；第三，解析弱信号识别的研究目标和数据来源；第四，梳理和讨论弱信号研究方法的适用性；第五，提出弱信号识别研究的发展方向。文献综述的结果表明，目前弱信号的定义尚未统一，识别理论尚未成熟，研究数据多源，研究类型丰富，研究方法较为多样，识别结果的评估和检验尚不充分，这为学界研究弱信号提供了方向。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	韩盟
	陈悦
	王玉奇
	宋凯
	王康

关键词 ：弱信号识别, 行业发展, 技术监测, 危机预警, 产业融合

收稿日期: 2022-10-30

基金资助:国家重点研发计划项目“颠覆性技术识别理论、方法与专家预判系统”（2019YFA0707201）；辽宁省科技创新智库研究基地“未来技术与产业协同发展研究基地”项目（ZX20211063）；教育部哲学社会科学研究重大课题攻关项目“基础研究领域颠覆性科研成果识别与我国基础研究能力提升研究”（22JZD021）。

作者简介: 韩盟，男，1996年生，博士研究生，研究方向为科学计量、科技管理；陈悦，通信作者，女，1975年生，博士，教授，博士生导师，研究方向为科学计量学、科学学与科技创新管理，E-mail：chenyuedlut@163.com；王玉奇，男，1994年生，博士研究生，研究方向为科学计量、科技管理；宋凯，女，1993年生，博士研究生，研究方向为科学计量学、机器学习、自然语言处理；王康，男，1992年生，博士研究生，研究方向为科学计量学、技术预见；

引用本文:

韩盟, 陈悦, 王玉奇, 宋凯, 王康. 弱信号识别研究综述：寻找微弱的未来信号[J]. 情报学报, 2023, 42(8): 996-1008.
Han Meng, Chen Yue, Wang Yuqi, Song Kai, Wang Kang. Searching for Weak Future Signals: A Review of Weak Signal Identification. 情报学报, 2023, 42(8): 996-1008.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2023.08.010 或 https://qbxb.istic.ac.cn/CN/Y2023/V42/I8/996

1 Reger G. Technology foresight in companies: from an indicator to a network and process perspective[J]. Technology Analysis & Strategic Management, 2001, 13(4): 533-553.
2 赵小康. 弱信号: 识别、探测与应对[J]. 情报杂志, 2010, 29(1): 159-163.
3 Ansoff H I. Managing strategic surprise by response to weak signals[J]. California Management Review, 1975, 18(2): 21-33.
4 Ansoff H I. Strategic issue management[J]. Strategic Management Journal, 1980, 1(2): 131-148.
5 Coffman B S. Weak signal research[EB/OL]. (1997-01-15) [2022-04-23]. https://legacy.mgtaylor.com/mgtaylor/jotm/winter97/wsrintro.htm.
6 Hiltunen E. The future sign and its three dimensions[J]. Futures, 2008, 40(3): 247-260.
7 Blanco S, Lesca H. Business intelligence: integrating knowledge into the selection of early warning signals[OL]. (1998-01-01) [2022-04-21]. http://www.veille-strategique.org/docs/1998-Blanco-Lesca-Articlewksp.pdf.
8 Uskali T. Paying attention to weak signals - the key concept for Innovation Journalism[J/OL]. Innovation Journalism, 2005, 2(11). (2005-08-30). http://journal.innovationjournalism.org/2005/08/paying-attention-to-weak-signals-key.html?m=0.
9 沈固朝. 信号分析: 竞争情报研究的又一重要课题[J]. 图书情报工作, 2009, 53(20): 11-14, 59.
10 Mendon?a S, Cardoso G, Cara?a J. The strategic strength of weak signal analysis[J]. Futures, 2012, 44(3): 218-228.
11 单彬. 认知视角下的弱信号分析及实证研究[D]. 北京: 中国人民解放军军事医学科学院, 2014.
12 Kim J, Lee C Y. Novelty-focused weak signal detection in futuristic data: assessing the rarity and paradigm unrelatedness of signals[J]. Technological Forecasting and Social Change, 2017, 120: 59-76.
13 董尹, 刘千里, 宋继伟, 等. 弱信号研究综述: 概念、方法和工具[J]. 情报理论与实践, 2018, 41(10): 147-154.
14 Mendon?a S, Cunha M P, Kaivo-oja J, et al. Wild cards, weak signals and organisational improvisation[J]. Futures, 2004, 36(2): 201-218.
15 Schultz W L. Infinite futures: foresight training and facilitation[EB/OL]. (2002-04-01) [2022-04-21]. http://infinitefutures.com/essays/prez/holescan/sld005.htm.
16 Griol-Barres I, Milla S, Cebrián A, et al. Detecting weak signals of the future: a system implementation based on text mining and natural language processing[J]. Sustainability, 2020, 12(19): 7848.
17 Inayatullah S. Futures visions for South-east Asia: some early warning signals[J]. Futures, 1995, 27(6): 681-688.
18 Tu Y N, Seng J L. Indices of novelty for emerging topic detection[J]. Information Processing & Management, 2012, 48(2): 303-325.
19 Chen C M. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature[J]. Journal of the American Society for Information Science and Technology, 2006, 57(3): 359-377.
20 Weismayer C, Pezenka I. Identifying emerging research fields: a longitudinal latent semantic keyword analysis[J]. Scientometrics, 2017, 113(3): 1757-1785.
21 An L, Lin X, Yu C M, et al. Measuring and visualizing the contributions of Chinese and American LIS research institutions to emerging themes and salient themes[J]. Scientometrics, 2015, 105(3): 1605-1634.
22 卢超, 侯海燕, DingYing, 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.
23 Ansoff H I. Implanting strategic management[M]. Englewood Cliffs: Prentice Hall International, 1984.
24 托马斯·西比奥克, 马塞尔·德尼西. 意义的形式: 建模系统理论与符号学分析[M]. 余红兵, 译. 成都: 四川大学出版社, 2016.
25 Tabatabaei N. Detecting weak signals by internet-based environmental scanning[D]. Waterloo: University of Waterloo, 2011.
26 Lee Y J, Park J Y. Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence[J]. Quality & Quantity, 2018, 52(2): 653-667.
27 Robinson C V, Ahmad F, Simmons J E L. Consolidation and fragmentation in environmental scanning: a review and research agenda[J]. Long Range Planning, 2021, 54(3): 101997.
28 Amanatidou E, Butter M, Carabias V, et al. On concepts and methods in horizon scanning: lessons from initiating policy dialogues on emerging issues[J]. Science and Public Policy, 2012, 39(2): 208-221.
29 Decker R, Wagner R, Scholz S W. An internet-based approach to environmental scanning in marketing planning[J]. Marketing Intelligence & Planning, 2005, 23(2): 189-199.
30 术语在线. 适应性地形[EB/OL]. [2023-07-11]. https://www.termonline.cn/word/239117/1#s1.
31 Yoo S H, Won D. Simulation of weak signals of nanotechnology innovation in complex system[J]. Sustainability, 2018, 10(2): 486-499.
32 邓锦峰, 吴建华, 袁勤俭. 信息觅食理论的演化及其应用[J]. 情报理论与实践, 2021, 44(12): 65-72.
33 张玥, 赵恬. 基于意义建构理论的移动互联网深阅读素养培育策略研究[J]. 情报理论与实践, 2021, 44(7): 65-70, 85.
34 Yoon J. Detecting weak signals for long-term business opportunities using text mining of Web news[J]. Expert Systems with Applications, 2012, 39(16): 12543-12550.
35 Thorleuchter D, van den Poel D. Weak signal identification with semantic web mining[J]. Expert Systems with Applications, 2013, 40(12): 4978-4985.
36 Griol-Barres I, Milla S, Millet J. Improving strategic decision making by the detection of weak signals in heterogeneous documents by text mining techniques[J]. AI Communications, 2019, 32(5/6): 347-360.
37 Saritas O, Burmaoglu S. The evolution of the use of Foresight methods: a scientometric analysis of global FTA research output[J]. Scientometrics, 2015, 105(1): 497-508.
38 Hoisl K, Stelzer T, Biala S. Forecasting technological discontinuities in the ICT industry[J]. Research Policy, 2015, 44(2): 522-532.
39 Li M N, Porter A L. Facilitating the discovery of relevant studies on risk analysis for three-dimensional printing based on an integrated framework[J]. Scientometrics, 2018, 114(1): 277-300.
40 Villasalero M. Signaling, spillover and learning effects of knowledge flows on division performance within related diversified firms[J]. Journal of Knowledge Management, 2013, 17(6): 928-942.
41 Thorleuchter D, van den Poel D. Semantic compared cross impact analysis[J]. Expert Systems with Applications, 2014, 41(7): 3477-3483.
42 胡雅萍, 董尹. 基于信号分析的决策失误风险识别与应对[J]. 情报杂志, 2022, 41(4): 22-28.
43 Thorleuchter D, Scheja T, van den Poel D. Semantic weak signal tracing[J]. Expert Systems with Applications, 2014, 41(11): 5009-5016.
44 邓胜利, 林艳青, 王野. 企业竞争弱信号的特征提取与定量识别研究[J]. 图书情报工作, 2016, 60(10): 67-75.
45 Griol-Barres I, Milla S, Millet J. System implementation for the detection of weak signals of the future in heterogeneous documents by text mining and natural language processing techniques[C]// Proceedings of the 11th International Conference on Agents and Artificial Intelligence. Setúbal: Science and Technology Publications, 2019: 631-638.
46 Roh S, Choi J Y. Exploring signals for a nuclear future using social big data[J]. Sustainability, 2020, 12(14): 5563.
47 Park C, Cho S. Analysis on trends and future signs of smart grids[J]. International Journal of Smart Grid and Clean Energy, 2020, 9(3): 533-543.
48 Bouktaib A, Fennan A. A framework for weak signal detection in competitive intelligence using semantic clustering algorithms[J]. International Journal of Advanced Computer Science and Applications, 2021, 12(12): 555-565.
49 Kim S, Kim Y E, Bae K J, et al. NEST: a quantitative model for detecting emerging trends using a global monitoring expert network and Bayesian network[J]. Futures, 2013, 52: 59-73.
50 Krigsholm P, Riekkinen K. Applying text mining for identifying future signals of land administration[J]. Land, 2019, 8(12): 181.
51 翟东升, 夏军, 张杰, 等. 基于专利新兴技术弱信号识别方法研究[J]. 情报杂志, 2015, 34(8): 31-36.
52 王康, 陈悦, 宋超, 等. 颠覆性技术: 概念辨析与特征分析[J]. 科学学研究, 2022, 40(11): 1937-1946.
53 刘亚辉, 许海云. 突破性创新早期识别与弱信号分析综述[J]. 图书情报工作, 2021, 65(4): 89-101.
54 Koivisto R, Kulmala I, Gotcheva N. Weak signals and damage scenarios—Systematics to identify weak signals and their sources related to mass transport attacks[J]. Technological Forecasting and Social Change, 2016, 104: 180-190.
55 El Akrouchi M, Benbrahim H, Kassou I. End-to-end LDA-based automatic weak signal detection in web news[J]. Knowledge-Based Systems, 2021, 212: 106650.
56 El Akrouchi M, Benbrahim H, Kassou I. Monitoring early warning signs evolution through time[C]// Proceedings of the 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence. New York: ACM Press, 2020: Article No.41.
57 Kwon L N, Park J H, Moon Y H, et al. Weak signal detecting of industry convergence using information of products and services of global listed companies - focusing on growth engine industry in South Korea[J]. Journal of Open Innovation: Technology, Market, and Complexity, 2018, 4(1): 10.
58 Yu M X, Pasman H, Erraguntla M, et al. A framework to identify and respond to weak signals of disastrous process incidents based on FRAM and machine learning techniques[J]. Process Safety and Environmental Protection, 2022, 158: 98-114.
59 Edo-Osagie O, Smith G, Lake I, et al. Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance[J]. PLoS One, 2019, 14(7): e0210689.
60 苗红, 郭鑫, 吴菲菲. 产业融合弱信号识别研究——以老年智能家居领域为例[J]. 情报杂志, 2020, 39(11): 54-60, 89.
61 Miao H, Guo X, Yuan F. Research on identification of potential directions of artificial intelligence industry from the perspective of weak signal[J/OL]. IEEE Transactions on Engineering Management, (2021-12-01). https://doi.org/10.1109/TEM.2021.3123639.