|
|
|
| New Approach for the Dynamic Evolution Analysis of Technology Topics: DPL-BMM |
| Song Kai, Chen Yue |
| Institution of Science of Science and S&T Management & WISE Lab, Dalian University of Technology, Dalian 116024 |
|
|
|
|
Abstract Understanding the pulse of technological evolution is important to understand the rules of technological development. Theme mining based on patent information is an effective way to present macroscopic laws based on the microscopic mechanisms of technological development, which are of great significance to technology overlays and innovation-driven practices. In this study, we propose a model for tracking the dynamic evolution of technology topics based on the DPL-BMM. This model is a Dirichlet process biterm-based mixture model with automatic labeling. This addresses the problem of a fixed number of topics in informatics. A topic representation module was added to identify specific technological topics. The approach was applied to the analysis of patent data in artificial intelligence, and the empirical results show that the method has practical application value for understanding technical topics and their evolution.
|
|
Received: 20 February 2023
|
|
|
|
1 Heiss M, Jankowsky J. The technology tree concept - an evolutionary approach to technology management in a rapidly changing market[C]// IEMC’01 Proceedings: Change Management and the New Industrial Revolution. Piscataway: IEEE, 2001: 37-43.
2 Yoon B, Phaal R, Probert D. Morphology analysis for technology roadmapping: application of text mining[J]. R&D Management, 2008, 38(1): 51-68.
3 Verspagen B. Mapping technological trajectories as patent citation networks: a study on the history of fuel cell research[J]. Advances in Complex Systems, 2007, 10(1): 93-115.
4 Fontana R, Nuvolari A, Verspagen B. Mapping technological trajectories as patent citation networks: an application to data communication standards[J]. Economics of Innovation and New Technology, 2009, 18(4): 311-336.
5 Martinelli A. An emerging paradigm or just another trajectory? Understanding the nature of technological changes using engineering heuristics in the telecommunications switching industry[J]. Research Policy, 2012, 41(2): 414-429.
6 Zhou X, Zhang Y, Porter A L, et al. A patent analysis method to trace technology evolutionary pathways[J]. Scientometrics, 2014, 100(3): 705-721.
7 黄颖, 叶冬梅, 丁凤, 等. 技术演化路径识别: 内涵释义与研究进展[J]. 图书情报工作, 2022, 66(22): 142-154.
8 高楠, 高嘉骐, 陈洪璞. 新兴技术识别与演化路径分析方法研究——以集成电路领域为例[J]. 情报科学, 2023, 41(3): 127-135, 172.
9 吴菲菲, 张亚茹, 黄鲁成, 等. 基于AToT模型的技术主题多维动态演化分析——以石墨烯技术为例[J]. 图书情报工作, 2017, 61(5): 95-102.
10 陈亮, 张静, 张海超, 等. 层次主题模型在技术演化分析上的应用研究[J]. 图书情报工作, 2017, 61(5): 103-108.
11 丰米宁, 魏凤, 李健, 等. 产业链视角下的主题识别与技术演化研究——以3D打印领域为例[J]. 情报杂志, 2020, 39(8): 46-52, 62.
12 吕璐成, 周健, 王学昭, 等. 基于双层主题模型的技术演化分析框架及其应用[J]. 数据分析与知识发现, 2022, 6(Z1): 18-32.
13 盛世豪. 试论技术进化和技术体系的演变[J]. 科学管理研究, 1989, 7(3): 44-47.
14 Blei D M, Lafferty J D. Dynamic topic models[C]// Proceedings of the 23rd International Conference on Machine Learning. New York: ACM Press, 2006: 113-120.
15 Chen J Y, Gong Z G, Liu W W. A Dirichlet process biterm-based mixture model for short text stream clustering[J]. Applied Intelligence, 2020, 50(5): 1609-1619.
16 Hartigan J A, Wong M A. Algorithm AS 136: a k-means clustering algorithm[J]. Journal of the Royal Statistical Society, Series C (Applied Statistics), 1979, 28(1): 100-108.
17 Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation[J]. Journal of Machine Learning Research, 2003, 3(4-5): 993-1022.
18 Yin J H, Chao D R, Liu Z K, et al. Model-based clustering of short text streams[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: ACM Press, 2018: 2634-2642.
19 Pitman J. Poisson-Dirichlet and GEM invariant distributions for split-and-merge transformations of an interval partition[J]. Combinatorics, Probability and Computing, 2002, 11(5): 501-514.
20 王秀红, 高敏. 基于BERT-LDA的关键技术识别方法及其实证研究——以农业机器人为例[J]. 图书情报工作, 2021, 65(22): 114-125.
21 Rose S, Engel D, Cramer N, et al. Automatic keyword extraction from individual documents[M]// Text Mining: Applications and Theory. Chichester: John Wiley & Sons, 2010: 1-20.
22 Mauri M, Elli T, Caviglia G, et al. RAWGraphs: a visualisation platform to create open outputs[C]// Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter. New York: ACM Press, 2017: Article No.28.
23 陈悦, 王康, 宋超, 等. 基于技术融合视角下的人工智能技术嵌入态势研究[J]. 科学学研究, 2021, 39(8): 1448-1458.
24 刘则渊, 陈悦. 现代科学技术与发展导论[M]. 2版. 大连: 大连理工大学出版社, 2011.
25 Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database[C]// Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 248-255.
26 Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2012: 1097-1105.
27 R?der M, Both A, Hinneburg A. Exploring the space of topic coherence measures[C]// Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2015: 399-408. |
|
|
|
