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
[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.
曹琨, 吴新年, 靳军宝, 郑玉荣, 付爽. 基于共词和Node2Vec表示学习的新兴技术识别方法*[J]. 数据分析与知识发现, 2023, 7(9): 89-99.
Cao Kun, Wu Xinnian, Jin Junbao, Zheng Yurong, Fu Shuang. Identification of Emerging Technology Based on Co-words and Node2Vec Representation Learning. Data Analysis and Knowledge Discovery, 2023, 7(9): 89-99.
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
(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
(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.)
(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
(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
(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.)
(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.)
(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.)
(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
(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
(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.)
(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
(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.)
(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.)
(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.)
(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.)
(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.)
(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.
(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
(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
(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.)
