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| Identification of Emerging Technology Based on Co-words and Node2Vec Representation Learning |
Cao Kun1,2,Wu Xinnian1,2( ),Jin Junbao1,2,Zheng Yurong1,Fu Shuang1 |
1Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2Department of Information Resource Managements, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China |
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Abstract [Objective] This paper aims to efficiently and accurately identify emerging technologies, which also helps governments and enterprises allocate resources appropriately. [Methods] We took fine-grained technical terms as research objects. We constructed an emerging technology recognition model based on the co-word network’s structural features and semantic representation. Then, we identified emerging terms and quantified their scores. Third, we used the Node2Vec graph representation learning algorithm to encode and semantically represent the vectors of these terms. Finally, we identified emerging terms and technical topics. [Results] We conducted an empirical study with the new model and CNC machine tools. A total of 449 emerging terms and four emerging technology topics (including robot automatic loading and unloading systems, clean and efficient cutting technology, high-speed and high-precision CNC machining centers, and additive-subtractive hybrid manufacturing technology) were identified. [Limitations] We only used patent data, which needs to be expanded to other multi-source heterogeneous data with network relationships like citation and semantic similarity. [Conclusions] Using the co-word and Node2Vec representation learning method, we successfully utilize the co-word network’s structural features and semantic representation between technical terms, which help us identify emerging technologies.
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Received: 12 October 2022
Published: 24 October 2023
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| Fund:The National Social Science Fund of China(20BTQ094);The Soft Science Project of Gansu Province(21CX6ZA110) |
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Corresponding Authors:
Wu Xinnian，ORCID：0000-0002-7865-9548，E-mail：wuxn@lzb.ac.cn。
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| [1] |
Lee M H, Kim S, Kim H, et al. Technology Opportunity Discovery Using Deep Learning-Based Text Mining and a Knowledge Graph[J]. Technological Forecasting and Social Change, 2022, 180: 121718.
doi: 10.1016/j.techfore.2022.121718
|
| [2] |
Miao H, Wang Y, Li X, et al. Integrating Technology-Relationship-Technology Semantic Analysis and Technology Roadmapping Method: A Case of Elderly Smart Wear Technology[J]. IEEE Transactions on Engineering Management, 2022, 69(1): 262-278.
doi: 10.1109/TEM.2020.2970972
|
| [3] |
王玏, 吴新年. 新兴技术识别方法研究综述[J]. 图书情报工作, 2020, 64(4): 125-135.
doi: 10.13266/j.issn.0252-3116.2020.04.014
|
| [3] |
(Wang Le, Wu Xinnian. Research on Identification Methods of Emerging Technologies[J]. Library and Information Service, 2020, 64(4): 125-135.)
doi: 10.13266/j.issn.0252-3116.2020.04.014
|
| [4] |
李昌, 杨中楷, 董坤. 基于多维属性动态变化特征的新兴技术识别研究[J]. 情报学报, 2022, 41(5): 463-474.
|
| [4] |
(Li Chang, Yang Zhongkai, Dong Kun. Recognition of Emerging Technologies Based on Dynamic Characteristics of Multi-dimensional Attributes[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(5): 463-474.)
|
| [5] |
乔治·戴, 保罗·休梅克. 沃顿论新兴技术管理[M]. 石莹, 等译. 北京: 华夏出版社, 2002.
|
| [5] |
(Geroge S. Day, Paul J. H. Schoemaker. Wharton on Managing Emerging Technologies[M]. Translated by ShiYing, et al. Beijing: Huaxia Publishing House, 2002.)
|
| [6] |
Rotolo D, Hicks D, Martin B R. What is an Emerging Technology?[J]. Research Policy, 2015, 44(10): 1827-1843.
doi: 10.1016/j.respol.2015.06.006
|
| [7] |
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.
doi: 10.1016/j.techfore.2018.04.016
|
| [8] |
张金柱, 王秋月, 仇蒙蒙. 颠覆性技术识别研究进展综述[J]. 数据分析与知识发现, 2022, 6(7): 12-31.
|
| [8] |
(Zhang Jinzhu, Wang Qiuyue, Qiu Mengmeng. Review of Studies Identifying Disruptive Technologies[J]. Data Analysis and Knowledge Discovery, 2022, 6(7): 12-31.)
|
| [9] |
Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. The Journal of Machine Learning Research, 2003, 3: 993-1022.
|
| [10] |
Xu S, Hao L Y, Yang G C, et al. A Topic Models Based Framework for Detecting and Forecasting Emerging Technologies[J]. Technological Forecasting and Social Change, 2021, 162: 120366.
doi: 10.1016/j.techfore.2020.120366
|
| [11] |
宋凯, 朱彦君. 专利前沿技术主题识别及趋势预测方法——以人工智能领域为例[J]. 情报杂志, 2021, 40(1): 33-38.
|
| [11] |
(Song Kai, Zhu Yanjun. Patent Frontier Technology Topic Identification and Trend Prediction: A Case Analysis of Artificial Intelligence[J]. Journal of Intelligence, 2021, 40(1): 33-38.)
|
| [12] |
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.
|
| [12] |
(Tang Heng, Qiu Yuewen. Emerging Technology Topic Identification Based on Multi-Source Information: Intelligent Connected Vehicle as an Example[J]. Journal of Intelligence, 2021, 40(3): 81-88.)
|
| [13] |
任佳妮, 张薇, 杨阳, 等. “人工智能+医疗”新兴技术识别研究——以医疗机器人为例[J]. 情报杂志, 2021, 40(12): 45-50.
|
| [13] |
(Ren Jiani, Zhang Wei, Yang Yang, et al. Research on Identification of Emerging Technologies of “Artificial Intelligence+Medical”—As the Case of Medical Robot[J]. Journal of Intelligence, 2021, 40(12): 45-50.)
|
| [14] |
李慧, 胡吉霞, 佟志颖. 面向多源数据的学科主题挖掘与演化分析[J]. 数据分析与知识发现, 2022, 6(7): 44-55.
|
| [14] |
(Li Hui, Hu Jixia, Tong Zhiying. Subject Topic Mining and Evolution Analysis with Multi-Source Data[J]. Data Analysis and Knowledge Discovery, 2022, 6(7): 44-55.)
|
| [15] |
Stein R A, Jaques P A, Valiati J F. An Analysis of Hierarchical Text Classification Using Word Embeddings[J]. Information Sciences, 2019, 471: 216-232.
doi: 10.1016/j.ins.2018.09.001
|
| [16] |
周云泽, 闵超. 基于LDA模型与共享语义空间的新兴技术识别——以自动驾驶汽车为例[J]. 数据分析与知识发现, 2022, 6(2/3): 55-66.
|
| [16] |
(Zhou Yunze, Min Chao. Identifying Emerging Technology with LDA Model and Shared Semantic Space——Case Study of Autonomous Vehicles[J]. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 55-66.)
|
| [17] |
Tshitoyan V, Dagdelen J, Weston L, et al. Unsupervised Word Embeddings Capture Latent Knowledge from Materials Science Literature[J]. Nature, 2019, 571(7763): 95-98.
doi: 10.1038/s41586-019-1335-8
|
| [18] |
Cui P, Wang X, Pei J, et al. A Survey on Network Embedding[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(5): 833-852.
doi: 10.1109/TKDE.69
|
| [19] |
丁恒, 任卫强, 曹高辉. 基于无监督图神经网络的学术文献表示学习研究[J]. 情报学报, 2022, 41(1): 62-72.
|
| [19] |
(Ding Heng, Ren Weiqiang, Cao Gaohui. Using Unsupervised Graphs of Neural Networks for Constructing Learning Representations of Academic Papers[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(1): 62-72.)
|
| [20] |
孙蒙鸽, 王燕鹏, 韩涛, 等. 新兴技术的多指标量化识别研究——基于向量表征方法的探索[J]. 图书情报工作, 2022, 66(3): 130-139.
doi: 10.13266/j.issn.0252-3116.2022.03.014
|
| [20] |
(Sun Mengge, Wang Yanpeng, Han Tao, et al. Research on Multi-Index Quantitative Recognition of Emerging Technologies: Exploration Based on Vector Representation Method[J]. Library and Information Service, 2022, 66(3): 130-139.)
doi: 10.13266/j.issn.0252-3116.2022.03.014
|
| [21] |
霍朝光, 魏瑞斌, 张斌. 基于PageRank和Node2vec的研究热点与集群发现——以国际深度学习研究领域为例[J]. 情报杂志, 2020, 39(8): 174-179, 153.
|
| [21] |
(Huo Chaoguang, Wei Ruibin, Zhang Bin. Research on Discovery of Hot Topic and Cluster Based on PageRank and Node2vec: International Deep Learning Research as an Example[J]. Journal of Intelligence, 2020, 39(8): 174-179, 153.)
|
| [22] |
翟东升, 阚慧敏, 李梦洋, 等. 产业链视角下基于图嵌入的专利布局意图挖掘方法研究[J]. 情报学报, 2022, 41(5): 437-450.
|
| [22] |
(Zhai Dongsheng, Kan Huimin, Li Mengyang, et al. Research on Mining Patent Layout Intention Based on Graph Embedding from the Perspective of Industrial Chain[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(5): 437-450.)
|
| [23] |
杜瑾, 熊回香, 王妞妞. 融合多元网络与网络表示学习的科研合作者推荐研究[J]. 情报资料工作, 2022, 43(4): 27-35.
|
| [23] |
(Du Jin, Xiong Huixiang, Wang Niuniu. Research Collaborator Recommendation Research on Fusion of Multivariate Networks and Network Representation Learning[J]. Information and Documentation Services, 2022, 43(4): 27-35.)
|
| [24] |
周源, 刘宇飞, 薛澜. 一种基于机器学习的新兴技术识别方法:以机器人技术为例[J]. 情报学报, 2018, 37(9): 939-955.
|
| [24] |
(Zhou Yuan, Liu Yufei, Xue Lan. An Approach to Identify Emerging Technologies Using Machine Learning: A Case Study of Robotics[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(9): 939-955.)
|
| [25] |
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.
|
| [25] |
(Song Xinna, Guo Ying, Xi Xiaowen. Research on Multi-Indicator Emerging Technology Identification Based on Patent Literature[J]. Journal of Intelligence, 2020, 39(6): 76-81, 88.)
|
| [26] |
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.
|
| [26] |
(Liu Ziqiang, Hu Zhengyin, Xu Haiyun, et al. Identifying and Visualizing Emerging Trends in Domain Based on PWLR Model[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(9): 979-988.)
|
| [27] |
Kim Y G, Suh J H, Park S C. Visualization of Patent Analysis for Emerging Technology[J]. Expert Systems with Applications, 2008, 34(3): 1804-1812.
doi: 10.1016/j.eswa.2007.01.033
|
| [28] |
Huang L, Chen X, Ni X X, et al. Tracking the Dynamics of Co-word Networks for Emerging Topic Identification[J]. Technological Forecasting and Social Change, 2021, 170: 120944.
doi: 10.1016/j.techfore.2021.120944
|
| [29] |
Arthur W B. The Nature of Technology: What it is and How It Evolves[M]. New York: Free Press, 2009.
|
| [30] |
Jin Q C, Jiang J, Li J C, et al. Emerging Technology Identification and Selection Based on Data-Driven: Taking the Unmanned Systems as an Example[C]// Proceedings of 2020 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 2020: 1380-1384.
|
| [31] |
邓启平, 陈卫静, 张玲玲, 等. 基于多维特征测度的人工智能领域研究前沿分析[J]. 情报杂志, 2020, 39(3): 56-62.
|
| [31] |
(Deng Qiping, Chen Weijing, Zhang Lingling, et al. Research Fronts Analysis of AI Based on Multidimensional Feature Measure[J]. Journal of Intelligence, 2020, 39(3): 56-62.)
|
| [32] |
Kwon S, Liu X Y, Porter A L, et al. Research Addressing Emerging Technological Ideas Has Greater Scientific Impact[J]. Research Policy, 2019, 48(9): 103834.
doi: 10.1016/j.respol.2019.103834
|
| [33] |
郭颖, 王明星, 段炜钰. 专利的技术新兴度与其技术影响力间关系研究[J]. 科学学研究, 2022, 40(6): 1034-1043.
|
| [33] |
(Guo Ying, Wang Mingxing, Duan Weiyu. A Study on the Relationship Between the Patent’s Technological Emerging Degree and Its Technology Impact[J]. Studies in Science of Science, 2022, 40(6): 1034-1043.)
|
| [34] |
Shannon C E. A Mathematical Theory of Communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423.
doi: 10.1002/bltj.1948.27.issue-3
|
| [35] |
Lucio-Arias D, Leydesdorff L. An Indicator of Research Front Activity: Measuring Intellectual Organization as Uncertainty Reduction in Document Sets[J]. Journal of the American Society for Information Science and Technology, 2009, 60(12): 2488-2498.
doi: 10.1002/asi.v60:12
|
| [36] |
Diakoulaki D. Mavrotas G. Papayannakis L. Determining Objective Weights in Multiple Criteria Problems: The Critic Method[J]. Computers and Operations Research, 1995, 22(7): 763-770.
doi: 10.1016/0305-0548(94)00059-H
|
| [37] |
Grover A, Leskovec J. node2vec: Scalable Feature Learning for Networks[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 855-864.
|
| [38] |
Dai H J, Umarov R, Kuwahara H, et al. Sequence2Vec: A Novel Embedding Approach for Modeling Transcription Factor Binding Affinity Landscape[J]. Bioinformatics, 2017, 33(22): 3575-3583.
doi: 10.1093/bioinformatics/btx480
pmid: 28961686
|
| [39] |
Zhou Y, Dong F, Liu Y F, 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.
doi: 10.1007/s11192-020-03797-8
|
| [40] |
刘林山, 李建永, 郝铭. 一种数控机床自动上下料桁架机器人控制系统设计与实现[J]. 制造业自动化, 2019, 41(9): 108-110, 138.
|
| [40] |
(Liu Linshan, Li Jianyong, Hao Ming. Research on Control System of Up-Down Materials Truss Robot for a CNC Machine[J]. Manufacturing Automation, 2019, 41(9): 108-110, 138.)
|
| [41] |
张明辰, 王坤. 数控机床加工中心技术研究[J]. 河南科技, 2019(14): 66-68.
|
| [41] |
(Zhang Mingchen, Wang Kun. Machining Center Technology of CNC Machine Tools[J]. Henan Science and Technology, 2019(14): 66-68.)
|
| [42] |
西门子数字化工业集团. 机械加工数字化技术白皮书[R]. 2021: 26-28.
|
| [42] |
(Siemens Digital Industry Group. White Paper on Machining Digital Technology[R]. 2021: 26-28.)
|
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