=Paper=
{{Paper
|id=Vol-2871/paper14
|storemode=property
|title=Emerging Technology Opportunity Identification Based on Community Detection and Burst Detection: A Case study of Intelligent Robots
|pdfUrl=https://ceur-ws.org/Vol-2871/paper14.pdf
|volume=Vol-2871
|authors=Jingwen Luo,Fang Zou,Yu Zhang,Ying Huang
|dblpUrl=https://dblp.org/rec/conf/iconference/LuoZZH21
}}
==Emerging Technology Opportunity Identification Based on Community Detection and Burst Detection: A Case study of Intelligent Robots==
https://ceur-ws.org/Vol-2871/paper14.pdf
1st Workshop on AI + Informetrics - AII2021
Emerging Technology Opportunity Identification Based
on Community Detection and Burst Detection: A Case
study of Intelligent Robots
Jingwen Luo1,2, Zou Fang3, Yujie Peng4, Ying Huang1,5*
1. School of Information Management, Wuhan University, China
2. Information School, The University of Sheffield, United Kingdom
3. School of Public Administration, Hunan University, China )
4. Department of Management and Economics, North China University of Water Resources and
Electric Power, China
5. Centre for R&D Monitoring (ECOOM) and Department of MSI, KU Leuven, Belgium
Abstract: Due to a new round of technological revolution and industrial trans-
formation driven by artificial intelligence, emerging technologies have triggered
a new round of unprecedented developments. Understanding the technology de-
velopment trend and identifying potential technology opportunities has become
an essential proposition for academia and industry. Based on patent document
data, we apply text analysis to extract technical terms, introduce the community
detection model to technology topics, then construct a framework for identifying
opportunities for emerging technologies from three perspectives: technological
R&D trends, competitive environment, and technology layout. The intelligent
robot technology is illustrated as an empirical study to clarify the proposed frame-
work. We conclude three main findings: First, intelligent robot technology has
undergone three evolution stages and formed 16 key topics, among which IoT
robot, sensor fibre resistance, and pneumatic muscle have become hotspots in
recent years; Second, China has an absolute advantage in the scale of intelligent
robot technology, but it has a noticeable strength gap with other countries and
regions in core technologies; Third, compared to the other leading countries in
intelligent robots field, the layout of China's overseas technology market is rela-
tively limited, especially in remote control, robot voice and intelligent detection.
The proposed opportunity identification framework can clarify emerging tech-
nologies' overall context and provide a helpful reference for emerging technolo-
gies' market layout and technology layout.
Keywords: Intelligent Robot; Emerging Technology; Patent Analysis; Topic
Identification; Technology Opportunity Analysis
Copyright 2021 for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
1 Introduction
The technology's iteration is accelerating rapidly, with old technologies always being
eliminated and new ones emerging. Only by timely grasping the trend of emerging
technology development and taking the lead in occupying the advantageous layout of
emerging technology can the nations/enterprise win the competitive advantage in the
fierce technological and industrial revolution. An accurate grasp of emerging technol-
ogies' development trends relies on the effective identification and dynamic monitoring
of technology topics.
The pioneering researcher, Alan L. Porter from Georgia Institute of Technology,
who proposed the framework of Technology Opportunity Analysis (TOA), indicated
that analyzing the changes of related technology or the socio-economic environment in
a specific technology field through technology detection and bibliometrics can play a
particular role in predicting the technology development in this field (Porter et al.,
1995). TOA is a process of identifying emerging technologies and exploring promising
technologies in specific technology fields. Later, scholars research and elaborate TOA
based on Porter's systematic explanation, which can be mainly divided into three as-
pects:
Connotation and functions. Zhu et al. (1998) established an analysis system based
on data analysis and expert evaluation to provide the basis for monitoring and early
warning and government decision-making in specific technology fields. Yoon and
Park (2005) regarded TOA as one of the research methods of technology forecasting,
which is in line with many technology management researchers (Shane, 2000; Zhu
et al., 2002; Yang & Peiyang, 2013). Ma et al. (2014) defined technology oppor-
tunity as new technology forms that emerged from the existing technologies in a
specific field during technology development.
Methodology and indicators. Yoon et al. (2008) proposed a morphological analysis
based on keywords, using patent data to identify technology gaps as technology op-
portunities. Lee et al. (2009) used a combination of Principal Component Analysis
and keyword-based patent maps to identify technology opportunities in the map
gaps. Wang et al. (2015) applied text mining and a high-dimensional data object
clustering to papers and patents, exploring technology opportunities by mining the
gaps between science and technology. Rodriguez et al. (2016) discovered technology
opportunity by ranking patent outliers, identifying patent anomalies and patent at-
tributes and citations. Wang et al. (2017) proposed a morphological analysis based
on the Subject-Action-Object structure and morphological matrix. Lee et al. (2020)
used the word2vec method to navigate product landscape analysis to identify poten-
tial technology opportunities.
Empirical studies. Ho et al. (2014) summarized the main technological barriers and
the corresponding potential solutions for Proton Exchange Membrane Fuel Cells and
Direct Methanol Fuel Cells technologies. Ma et al. (2014) proposed a TOA frame-
work to analyze the technology opportunities in the dye-sensitized solar cells field.
Chan and Miyazaki (2015) explored the knowledge convergence between cloud
computing and big data and applied it to analyze Malaysia's emerging technology
opportunities. Zhang and Yu (2020) conducted analogies between source and target
domains to predict 5G technology opportunities.
Most of the above research on TOA focuses on the patent analysis of emerging tech-
nologies, which have narrower meanings and limited development time. In this paper,
we proposed a technology opportunity identification framework that can be applied to
a broader technology field by identifying the key technology topics and exploring the
evolution of the technology. We detected technology opportunities from multiple di-
mensions to examine the technology opportunities holistically.
In recent years, Artificial Intelligence (AI) has successfully sparked great interest
from all sectors of society. It is regarded as a current strategic high technology and a
stalwart in leading disruptive industry changes in the future. The combination of AI and
robots has made social productivity unprecedentedly magnified. Intelligent robots have
huge development prospects and are used in diversified scenarios.
Based on patent data of intelligent robots, we extracted terms and construct co-oc-
currence networks through text mining, apply community detection for technology
clustering, then introduce an emerging topic measurement model and an emergent topic
monitoring model to build a framework for identifying technology opportunities from
three perspectives: R&D trends, competitive environment, and technology layout. We
conducted an empirical study with the intelligent robots patents. We located intelligent
robots' key technologies and reveal the overall pulse of emerging technology develop-
ment using the framework, providing a useful reference for future emerging technolo-
gies' market layout and technology layout.
2 Framework and Methodology
2.1 Framework
Fig. 1 illustrated the framework of the research. First, we formulated the patent retrieval
query and downloaded the patent documents from the Derwent World Patents Index
(DWPI) database. Second, we used text analysis techniques to mine the patent data and
extract terms from the patent titles that can characterize the terms' full meaning, includ-
ing tokenization, POS tagging, stemming, lemmatization and merging terms with sim-
ilar semantics. Third, after constructing the term co-occurrence network, we used the
Leiden algorithm in the community detection to identify intelligent robots' key technol-
ogy topics; we used the burst detection algorithm to perform topic evolution and hotspot
identification of technical terms. Finally, based on the identified key technology topics,
we used the TOA framework to identify emerging technology opportunities from three
perspectives: technology R&D trends, competitive environment, and technology lay-
out.
Fig. 1. Research Framework
2.2 Methodology
Natural Language Processing.
Natural language processing (NLP), an important research subject in computer science
and artificial intelligence, mainly focuses on making computers understand natural hu-
man language. The Natural Language Toolkit (NLTK) package is a set of libraries and
programs for NLP developed by Bird et al. (2009). We used the NLTK to process the
patent titles, including tokenization, stopwords filtering, POS tagging, stemming, lem-
matization. We extracted n-gram terms from each patent title and performed fuzzy se-
mantic merging of terms with similar semantics.
Community Detection.
Community detection is used to understand the structure of large and complex net-
works. Many scholars have employed community detection in the identification of tech-
nology topics. Louvain algorithm by Blondel et al. (2008) as the representative hierar-
chical clustering algorithm is a commonly used community detection method. Louvain
algorithm is recognized as one of the fastest non-overlapping community detection al-
gorithms, but it may produce poorly connected communities. Traag et al. (2019) im-
proved the Louvain algorithm and introduced the Leiden algorithm, ensuring a good
connection between communities. We used the Leiden algorithm to conduct commu-
nity detection on the co-occurrence network. We obtained clearer clusters based on
word frequency and word contribution by removing some terms with little meaning
from the co-occurrence matrix.
Burst Detection
In 2002, Kleinberg (2003) proposed the Burst Detection algorithm, which can identify
the sudden increase or "burst" of terms' use frequency over time. Kleinberg used text
classification technology to classify documents that need to be detected. The classified
documents are defined as a time-sensitive sequence according to their arrival time.
When an event occurs in the real world, the event's documents increase, causing time
interval to become shorter. The state at this time is the Burst State, and the opposite is
the Normal State. The change of time interval can detect the transition from the Normal
State to the Burst State. In this way, the burst is defined as the transition between states,
and detect the burst is to detect the time interval between the arrival of two documents.
We used burst detection to identify bursts of term frequencies and obtain the burst hot
technology topics in this paper.
2.3 Data collection.
We collected patent data from DWPI, which contains patent data from patent licensing
agencies in over 100 countries and regions. It provides access to published patents and
scientific literature worldwide.
We initially formulated the retrieval query by reviewing relevant literature and con-
sulting domain experts; after repeated verification and justification, the final retrieval
query was set as follows: ABD=(((Autonomous OR Bio* OR Humanoid OR Smart OR
Intelligent) ADJ2 Robot*) OR Bio-robot* OR Biorobot* OR (Robot* Cognit*) OR
(Robot* Percept*) OR (Robot* Sens*) OR (Robot* Act*)). Considering the emergence
of intelligent robot patents and the time lag between patent application and publication,
we set the time interval from 1956 to 2018. A total of 10,433 intelligent robot-related
patents were retrieved (the retrieval was conducted on 20 February 2020).
3 Key Technologies Identification of Intelligent Robots
3.1 Clustering key technological topics
We extracted patent titles from these intelligent robot patent documents, employed text
mining, co-occurrence analysis, and community detection, and obtained 34 clusters, 33
of which are valid. We merged clusters sharing similar topics and finally got 16 topic
clusters, as shown in Fig. 2.
After manual analysis and interpretation, we obtained 16 key technologies: Human-
oid Robot, Robot Joint, Mechanical Arm, Driving Mechanism, Sensor, Wireless Com-
munication, Servo Motor, System Controller, Remote Control, Path Planning, Robot
Voice, Robot Vision, Detection Mechanism, Robot Interaction, Robotic Fish, and In-
telligent Robots for Different Usage (including Internet of Things Robots, Underwater
Robots, Sweeping Robots, Welding robots, Painting robots, etc.).
Fig. 2. Clustering Visualization of Intelligent Robot Technologies
3.2 Tracing the topic evolution
We used burst detection algorithms to analyze the rough topic evolutionary lineage of
intelligent robots over a more extended period of development. As shown in Fig. 3, we
broadly divided the evolution into three time periods: 1979-1993, 1994-2006, and
2006-2018. Between 1979 and 1993, robots were centred on 'Industrial Robots,' which
were mainly used in industrial manufacturing and were not yet characterized by intel-
ligence; from 1993 onwards, 'Autonomous Mobile Robots' emerged, which marked the
beginning of the transition from powerful stationary machines to highly autonomous
and complex mobile platforms. In 2002, the emergence of 'Pet Robots' signalled that
robots were no longer only used in industrial scenarios but were beginning to participate
in and change people's lives. It also meant that robots were becoming interactive, fol-
lowed by the "Humanoid Robot" in 2004 and the "Intelligent Mobile Robot" in 2006
when robots became associated with "intelligence" for the first time.
Fig. 3. The Evolution of Intelligent Robot Technologies Topics
The burst weights were calculated and ranked in descending order, and we selected
the highest-ranked topics that had not yet reached a steady-state as being related to burst
hotspots of intelligent robots.
Table 1. Burst Hotspots of Intelligent Robots
Burst Hotspots Weight Duration Start
Internet-of Thing 5.982 2 2017
Bionic Robot Fish Structure 5.431 3 2016
Charging Pile 5.156 1 2018
Multi-Modal Output 4.858 3 2016
Resistance Layer 4.448 2 2017
Intelligent Robot Human-Computer Interac-
4.399 2 2017
tion
Intelligent Security Robot 4.342 2 2017
Unmanned Aerial Vehicle 4.164 1 2018
Pneumatic Muscle 3.746 1 2018
Grabbing Robot 3.673 1 2018
As illustrated in Table 1, internet-of thing, bionic robot fish structure, robot charging
pile, multi-modal output, resistance layer, intelligent robot human-computer interac-
tion, intelligent security robot, unmanned aerial vehicle, pneumatic muscles, and grab-
bing robot have become hotspots in intelligent robots in recent years.
4 Key Technologies Opportunities Analysis of Intelligent Robot
4.1 R&D Trend Analysis
Although the earliest patent retrieved was published in 1977, the number of published
patents from 1975 to 1999 is deficient. For better presentation, Fig. 4 only shows the
growth trends for the 16 key technology topics for intelligent robots from 1999 to 2018.
As illustrated, Intelligent Robot for Different Usage has always been the main emphasis
of technology development, and the majority of key technologies have accelerated in
growth since 2014. Driving Mechanism, Humanoid Robots, Mechanical Arm, Robot
Vision, Path Planning, and Remote Control are rapidly developing. Still, the growth
trend shows that Humanoid Robot and Robot Vision have more momentum for future
development. Robot Voice, Sensor, Robot Interaction, System Controller, Wireless
Communication, Servo Motor, and Robot Joint are developing relatively slowly. In
terms of growth, System Controller and Robot Joint may step into a period of an upward
trend. Robotic Fish has a slower growth and fluctuating as a new technology, probably
because it has not yet stabilized. There is more potential for future growth.
Fig. 4. Analysis of the R&D Trend of Key Technologies of Intelligent Robot
4.2 Competitive Environment Analysis
Fig. 5(a) shows the strength comparison in terms of patent scale in the field of key
technologies of intelligent robots in five main competing countries/regions, namely
China, Japan, Korea, the US, and Europe. Compared with other countries/regions,
China has an absolute advantage in the scale of intelligent robot patents. Also, the lay-
out of key technologies of the five countries/regions is similar to a certain extent.
As the absolute value of the number of patents in the five leading countries/regions
varies considerably, to further compare the layout of each key technology for intelligent
robots in these countries/regions, we standardized the data to reveal the proportional
distribution of each country itself in each technology area, as shown in Fig. 5(b).
(a) Patent Counts (b) Patent Proportion
Fig. 5. Distribution of 16 Key Technologies in Major Countries/Regions
As illustrated, all five countries/regions, especially China and South Korea, empha-
size Intelligent Robots for Different Usage. Also, Europe has the most considerable
presence in Remote Control; Japan featured prominently in Path Planning; South Korea
has the largest share of Wireless Communication; and the US distinguished itself on
Remote Control, Path Planning, and Driving Mechanism.
In addition, we explicitly introduced an analysis of the number of core patents to
assess the core technological strength of the five countries/regions and form a more
scientific understanding of the competitive environment. A core patent has a key posi-
tion in a particular technology field, making an outstanding contribution to technolog-
ical development or impacting other patents or technologies (Xu et al., 2014). There are
many discussions on the identification of core patents. Still, we mainly use three indi-
cators to measure core patents: the number of citations, the number of patent families,
and the number of claims.
4.3 Technologies Layout Analysis
To clarify the five leading countries/regions' technology layout, we constructed a ma-
trix of the distribution of patent priority countries/regions and patent family coun-
tries/areas for intelligent robot technologies, as shown in Table 2. Patent priority coun-
tries/regions indicate the technology source, while the countries/regions distribution of
patent families describe the technology's market layout.
Table 2. Patent Priority and Patent Family of Intelligent Robot Technologies in Main
Countries/Regions
US
China (7342) Japan (1475) Korea (890) Europe (617)
(1790)
China (6905) 6896 51 29 11 22
U.S. (1091) 168 1056 188 59 230
Japan (1111) 102 250 1086 47 86
Korea (737) 51 240 46 725 63
Europe (73) 21 49 41 17 72
As depicted, China is the most prominent source of technology and the largest technol-
ogy market. Still, as much as 93.93% (6896/7342 = 0.9393) of China's intelligent robots
patents are filed domestically. In other words, the proportion of China's overseas intel-
ligent robots patents is deficient, which may weaken China in future international mar-
ket competition.
Generally, overseas patents' technology content is higher, and the scale of overseas
patent layout can somehow reflect those countries/regions' technological competitive-
ness. The proportion of overseas patents in China is the lowest among the five coun-
tries/regions, implying that China should focus on quality and innovation while devel-
oping its patent scale to enhance its technological advancement and competitiveness to
gain a dominant global position in intelligent robots. Furthermore, the US, Europe, Ko-
rea, and Japan have few patent applications in China, with the highest number being
only 168 of the US, which indicates that the domestic market in China is relatively
saturated. China may need to explore opportunities overseas in the future.
The US's overseas homologous ratio is 41.01%, and its overseas patent applications
are mainly distributed in Japan and South Korea. Simultaneously, the number of patents
filed in the US by Japan and South Korea is also the highest except for their own coun-
tries, which indicates that the three countries have close patent technology ties. Alt-
hough the patent scale of Europe is small, the ratio of overseas patent applications is
significant, at 88.33%, in contrast to China's conservative inward-looking technology
layout. This indicates Europe has pioneering consciousness, attaching importance to
the overseas technology market and rapid technology marketization.
5 Conclusion
As an emerging technology that has been developing rapidly in recent years, intelligent
robots have gone through three evolutionary stages: "Industrial Robot," "Autonomous
Mobile Robot," and "Intelligent Robot." It has developed sixteen key technologies,
namely Humanoid Robot, Robot Joint, Mechanical Arm, Driving Mechanism, Sensor,
Wireless Communication, Servo Motor, System controller, Remote Control, Path Plan-
ning, Robot Voice, Robot Vision, Detection Mechanism, Robot Interaction, Robotic
Fish and Intelligent Robot for Different Usage. IoT Robot, Bionic Robot Fish Structure,
Robot Charging Pile, Multi-modal Output Data, Resistance Layer, Intelligent Robot
Human-Computer Interaction, Intelligent Security Robot, Unmanned Aerial Vehicle,
Pneumatic Muscle, and Grabbing Robot become burst hotspots recently.
Among the key technology topics of intelligent robots, the Intelligent Robot for Dif-
ferent Usage has been the focus, Humanoid Robot will develop rapidly in the coming
period, and Robotic Fish has more significant potential for development as a burst
hotspot that has not yet stabilized. Globally, China's intelligent robot patents have an
absolute advantage in terms of scale. However, its core patented technologies left much
to be desired, particularly in Remote Control and Path Planning. China's intelligent ro-
bots technology mainly filed domestically, lacking overseas markets, making China
underprivileged to compete in the international market in the future. China could invest
more in Remote Control, Path Planning, Robot Voice, Detection Mechanism, and Ro-
bot Joint in the future to seek a balanced layout.
This paper's main contributions are summarized below: 1) When extracting terms
from patent titles, instead of extracting individual words, n-gram term with thematic
significance are extracted, providing convenience for topic identification and hotspots
analysis. 2) We constructed a framework for analyzing intelligent robots' key technol-
ogies' opportunities from three perspectives: technology R&D trends, competitive en-
vironment, and technology layout. It considers the overall patent scale and introduces
core patents analysis so that the countries/regions' technological strengths can be as-
sessed more scientifically, and the competitive environment can be understood more
holistically.
Limitations of this paper: 1) When extracting terms from patent titles, the overly
long terms were not accurately segmented, so some of the terms not being counted in
the co-word analysis may affect the results of the topic clustering identification. 2) The
proposed framework was based solely on the patents' perspective, without considering
the impact of market and technology policies on technology opportunities. For a com-
petitive environment, we only assess the competition between countries at a macro
level, but the competition between specific individuals, such as commercial enterprises
and research institutions, was neglected.
References
1. Bird, S., Klein, E., & Loper, E. (2009). Natural Language Processing with Python. O'Reilly
Media Inc. https://doi.org/10.17509/ijal.v1i1.106
2. Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of
communities in large networks. Journal of Statistical Mechanics: Theory and Experiment.
https://doi.org/10.1088/1742-5468/2008/10/P10008
3. Yoon, B., & Park, Y. (2005). A systematic approach for identifying technology opportuni-
ties: Keyword-based morphology analysis. Technological Forecasting and Social Change.
https://doi.org/10.1016/j.techfore.2004.08.011
4. Chan, S. K., & Miyazaki, K. (2015). Knowledge convergence between cloud computing and
big data and analysis of emerging technological opportunities in Malaysia. Portland Inter-
national Conference on Management of Engineering and Technology.
https://doi.org/10.1109/PICMET.2015.7273134
5. Ho, J. C., Saw, E. C., Lu, L. Y. Y., & Liu, J. S. (2014). Technological barriers and research
trends in fuel cell technologies: A citation network analysis. Technological Forecasting and
Social Change. https://doi.org/10.1016/j.techfore.2013.06.004
6. Kleinberg, J. (2003). Bursty and Hierarchical Structure in Streams. Data Mining and
Knowledge Discovery. https://doi.org/10.1023/A:1024940629314
7. Lee, C., Jeon, D., Ahn, J. M., & Kwon, O. (2020). Navigating a product landscape for tech-
nology opportunity analysis: A word2vec approach using an integrated patent-product data-
base. Technovation. https://doi.org/10.1016/j.technovation.2020.102140
8. Lee, S., Yoon, B., & Park, Y. (2009). An approach to discovering new technology opportu-
nities: Keyword-based patent map approach. Technovation. https://doi.org/10.1016/j.tech-
novation.2008.10.006
9. Ma, T., Porter, A. L., Guo, Y., Ready, J., Xu, C., & Gao, L. (2014). A technology opportu-
nities analysis model: applied to dye-sensitized solar cells for China. Technology Analysis
and Strategic Management. https://doi.org/10.1080/09537325.2013.850155
10. Porter, Alan L, Jin, Xiao-Yin, Gilmour, Joseph E, Cunningham, Scott, Xu, Huaidong, Sta-
nard, Christopher, & Lin, Wang. (1994). Technology opportunities analysis: Integrating
technology monitoring, forecasting, and assessment with strategic planning. SRA Journal,
26(2), 21.
11. Rodriguez, A., Tosyali, A., Kim, B., Choi, J., Lee, J. M., Coh, B. Y., & Jeong, M. K. (2016).
Patent Clustering and Outlier Ranking Methodologies for Attributed Patent Citation Net-
works for Technology Opportunity Discovery. IEEE Transactions on Engineering Manage-
ment. https://doi.org/10.1109/TEM.2016.2580619
12. Shane, S. (2000). Prior Knowledge and the Discovery of Entrepreneurial Opportunities. Or-
ganization Science. https://doi.org/10.1287/orsc.11.4.448.14602
13. Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing
well-connected communities. Scientific Reports. https://doi.org/10.1038/s41598-019-
41695-z
14. Wang, M. Y., Fang, S. C., & Chang, Y. H. (2015). Exploring technological opportunities by
mining the gaps between science and technology: Microalgal biofuels. Technological Fore-
casting and Social Change. https://doi.org/10.1016/j.techfore.2014.07.008
15. Wang, X., Ma, P., Huang, Y., Guo, J., Zhu, D., Porter, A. L., & Wang, Z. (2017). Combining
SAO semantic analysis and morphology analysis to identify technology opportunities. Sci-
entometrics. https://doi.org/10.1007/s11192-017-2260-y
16. Xu, H., Yue, Z., Lei, B., & Fang, S. (2014). Core Patents Mining based on Cross Co-occur-
rence Analysis to Patent Technology-Effect Subject Terms and Citations. Library and In-
formation Service. https://doi.org/10.13266/j.issn.0252-3116.2014.04.010
17. Yang, L., & Peiyang, X. (2013). Analysis on Research Status Quo of Technology Oppor-
tunity at Home and Abroad. Information Research. https://doi.org/10.3969/j.issn.1005-
8095.2013.01.002
18. Yoon, B., Phaal, R., & Probert, D. (2008). Morphology analysis for technology roadmap-
ping: Application of text mining. R and D Management. https://doi.org/10.1111/j.1467-
9310.2007.00493.x
19. Zhang, J., & Yu, W. (2020). Early detection of technology opportunity based on analogy
design and phrase semantic representation. Scientometrics. https://doi.org/10.1007/s11192-
020-03641-z
20. Zhu, D., Porter, A. L., & Porter, A. L. (2002). Automated extraction and visualization of
information for technological intelligence and forecasting. Technological Forecasting and
Social Change. https://doi.org/10.1016/S0040-1625(01)00157-3
21. Zhu, D., Wu, C., & Mao, J. (1998). Analysis of Technology Opportunities Based on Data
Network Environment. Industrial Engineering Journal. 1(04), 14