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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (2/3): 18-32    DOI: 10.11925/infotech.2096-3467.2021.0908
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Technology Evolution Analysis Framework Based on Two-Layer Topic Model and Application
Lv Lucheng1,2,Zhou Jian3,Wang Xuezhao1,2,Liu Xiwen1,2()
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academic of Sciences, Beijing 100190, China
3Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100094, China
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Abstract  

[Objective] This paper constructs a new analysis framework for technology evolution, aiming to address the problems of the topic similarity calculation and manually setting the threshold to judge the correlation between window technology topics. [Methods] We established the new framework based on two layer topic model, which identified the dynamic topics using the LDA and NMF. Then, we evaluated the technical topic identification effects with the indicators of inner consistency and outer difference of the topics. Finally, we analyzed the evolution of technical topics from the perspectives of topic growth and importance. [Results] We examined our new method with data from the field of resources and environment. The two layer topic model based on NMF is more effective in dynamic topic recognition, and the analysis results of technology evolution can be verified from the list of breakthrough technologies released by MIT Technology Review. [Limitations] This paper only studies the development of technology from emergence to extinction, and does not examine the division, derivation and integration of technology. [Conclusions] The proposed method can automatically identify dynamic topics and analyze their evolution tracks using the literature. It has application value in scientific and technological information analysis.
Key words： Technology Evolution Analysis      Topic Model      S&T Literature Mining      NMF      Resource and Environment Field     
Received: 25 August 2021      Published: 14 April 2022
ZTFLH:  G254  
Fund:Projects of Strategy Research from Bureau of Planning and Strategy, Chinese Academy of Sciences(GHJ-ZLZX-2020-31-3)
Corresponding Authors: Liu Xiwen,ORCID：0000-0003-0820-3622     E-mail: liuxw@mail.las.ac.cn
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Lucheng Lv
Jian Zhou
Xuezhao Wang
Xiwen Liu

Cite this article:

Lv Lucheng, Zhou Jian, Wang Xuezhao, Liu Xiwen. Technology Evolution Analysis Framework Based on Two-Layer Topic Model and Application. Data Analysis and Knowledge Discovery, 2022, 6(2/3): 18-32.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0908     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I2/3/18

The Overall Framework of Method
The Framework of Two-Layers Topic Model
符号 意义
T 总时刻
D i 第 i个时刻的文档集合
V 所有时刻的文档集合的词项集合
d 单篇文档
w 单个词项
k i 第 i个时刻的文档集的主题个数
A i 第 i个时刻的文档集合的文档-词项矩阵, A i ∈ R D i × V i
W i 第 i个时刻的由主题模型得到的文档集合的文档-主题矩阵, W i ∈ R D i × k i
H i 第 i个时刻的由主题模型得到的文档集合的主题-词项矩阵, H i ∈ R k i × V i
k ' 第二层主题模型的动态主题个数
A ' 由所有 H i（ i ∈ 1 , T）合并得到的矩阵, A ' ∈ R ∑ i = 1 T k i × V
W ' 以 A '作为主题模型的输入得到的文档-主题矩阵, W ' ∈ R ∑ i = 1 T k i × k '
H ' 以 A '作为主题模型的输入得到的主题-词项矩阵, H ' ∈ R k ' × V
The Symbol Definition in Two-Layer Topic Model
The Weight Calculation Method of Dynamic Theme in Each Time Window
年份 论文数量
2010 959
2011 1 078
2012 1 103
2013 1 173
2014 1 260
2015 1 268
2016 1 391
2017 1 504
2018 1 703
2019 2 166
2020 1 580
The Distribution of WOS Paper
Result of the Optimal Number of Window Topics by TL-NMF
Result of the Optimal Number of Window Topics by TL-LDA
The Topic Consistence Average Value by TL-NMF and TL-LDA When Taking the Optimal Number of Window Topics
Result of the Optimal Number of Dynamic Topics by TL-NMF and TL-LDA
模型 主题一致性 InnerSim 主题差异度 OuterDif f W 2 V 主题差异度 OuterDif f JCD
TL-NMF 0.366 9 0.806 9 0.986 7
TL-LDA 0.351 3 0.714 5 0.992 8
Comparison of Model Effect
Top5 t 0 t 7 t 10 t 27 t 35 t 49
1 Climate Lithium GIS Network COVID Microalgae
2 Change Ion Batteries Support Vector Machine Prediction Coronavirus Biodiesel
3 Impacts Li Spatial Prediction Artificial Neural SARS Algae
4 Temperature Capacity Regression Algorithm COV Biodiesel Production
5 Climate Change Storage Logistic ANN Pandemic Biofuels
Examples of Dynamic Topics Based on TL-NMF
Top5 t 0 t 5 t 17 t 26 t 32 t 41
1 Membrane Bioreactor Trend Analysis Model COVID Membrane Fouling Hydraulic Fracture
2 Temporal Microgrids Water Holocene Aerobic Granular Sludge Neural Network
3 Flower Pollination Algorithm Monitor Climate Ecological Footprint Biosynthesis Electricity Market
4 Nanoscale Zero Water Management Carbon Mediterranean Density Functional Theory Transfer Learning
5 Artificial Bee Colony Statistics Temperature Soil Erosion Hydrogen Generation Surface Mass
Examples of Dynamic Topics Based on TL-LDA
The Evaluation Analysis of Top3 Dynamic Topics in Growth
The Evaluation Analysis of Top3 Dynamic Topics in Importance
The Growth and Importance Distribution of 50 Dynamic Topics
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