Category Archives: ST&I indicators

Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies

  • A hybrid approach to extract technical intelligence for technological opportunities analysis.
  • To blend topic modeling, SAO semantic analysis and machine learning.
  • An optimized LDA-based topic extraction model with high accuracy.
  • A case study on dye-sensitized solar cell.

With the advancement of science and the emergence of new technologies, technology opportunities analysis has attracted increasing attention from both society and academia. This study proposes a hybrid approach to integrate topic modeling, semantic SAO analysis, machine learning, and expert judgment, identifying technological topics and potential development opportunities. The systematical methodology is applied to analyze a set of 9,883 Derwent Innovation Index (DII) patents related to the dye-sensitized solar cell to present its potential contribution of technical intelligence for R&D management. Also, how the approach is validated and optimized is illustrated. The main contributions of this paper are two-fold. First, an optimized topic extraction model with high accuracy is constructed, considering both the patent classification codes and term location. Second, we integrate the topic modeling, SAO technique, and machine learning to explore semantic relationships among technological topics represented as a suite of terms. The methodology overcomes some drawbacks of the current studies. It can be used as a powerful tool for technological opportunities analysis.

https://doi.org/10.1016/j.techfore.2021.121159

Author(s): Tingting Ma, Xiao Zhou, Jia Liu, Zhenkai Lou, Zhaoting Hua, Ruitao Wang
Organization(s): Communication University of China, Beijing Wuzi University, Xidian University, Anhui University of Technology
Source: Technological Forecasting and Social Change
Year: 2021

Mapping technological innovation dynamics in artificial intelligence domains: Evidence from a global patent analysis (full-text)

Artificial intelligence (AI) is emerging as a technology at the center of many political, economic, and societal debates. This paper formulates a new AI patent search strategy and applies this to provide a landscape analysis of AI innovation dynamics and technology evolution. The paper uses patent analyses, network analyses, and source path link count algorithms to examine AI spatial and temporal trends, cooperation features, cross-organization knowledge flow and technological routes. Results indicate a growing yet concentrated, non-collaborative and multi-path development and protection profile for AI patenting, with cross-organization knowledge flows based mainly on interorganizational knowledge citation links.

Full-Text available at https://doi.org/10.1371/journal.pone.0262050

Author(s): Na Liu, Philip Shapira, Xiaoxu Yue, Jiancheng Guan
Organization(s): Shandong Technology and Business University, University of Manchester, Tsinghua University, University of Chinese Academy of Sciences
Source: PLoS ONE
Year: 2021

Policy interactions with research trajectories: The case of cyber-physical convergence in manufacturing and industrials

From the early 2010s, policymakers and firms in advanced industrial economies began introducing approaches to systemically exploit manufacturing and industrial data using the notion of cyber-physical convergence. Three innovation concepts have been especially highlighted: Smart Manufacturing, the Industrial Internet and Industrie 4.0. In parallel, academics have employed these concepts in numerous ways to advance their work. Despite this broad interest, precise definition and delineation of the cyber-physical convergence research domain have received little attention. Also missing is systematic knowledge on the interactions of these concepts with research trajectories. This paper fills these gaps by operationalising a newly constructed definition of convergence, and delineating the associated research domain into five data-centric capabilities: Monitoring, Analytics, Modelling and Simulation, Transmission and Security. A bibliometric analysis of the domain is then performed for 2010–2019. There are three findings. First, Analytics and Security have assumed strategic positions within the domain, coinciding with a “strategic turn” in policy. Second, backed by concerted policy and funding efforts, growth in Chinese scientific output has outpaced key competitors, including the U.S. and Germany. Finally, the patterns of promoting their works in terms of the three concepts differ significantly amongst U.S.-, Germany- and China-based authors, which mirrors the different policy discourses prevalent in those countries.

https://doi.org/10.1016/j.techfore.2021.121347

Author(s): Tausif Bordoloi, Philip Shapira, Paul Mativeng
Organization(s): The University of Manchester, University of Johannesburg
Source: Technological Forecasting and Social Change
Year: 2022

A methodology for identifying breakthrough topics using structural entropy

This research uses link prediction and structural-entropy methods to predict scientific breakthrough topics. Temporal changes in the structural entropy of a knowledge network can be used to identify potential breakthrough topics. This has been done by tracking and monitoring a network’s critical transition points, also known as tipping points. The moment at which a significant change in the structural entropy of a knowledge network occurs may denote the points in time when breakthrough topics emerge. The method was validated by domain experts and was demonstrated to be a feasible tool for identifying scientific breakthroughs early. This method can play a role in identifying scientific breakthroughs and could aid in realizing forward-looking predictions to provide support for policy formulation and direct scientific research. Notes on methodology: First, text data were imported into Clarivate’s Derwent Data Analyzer, and the multi-word list in the field of “combined keywords + phrase” was selected as the field-of-topic term. The list in the “combined keywords + phrase” field was extracted from titles…

Highlights

Identifying a scientific breakthrough early and helping to establish forward-looking predictions.

Depicting the non-linear characteristics of complex knowledge networks through structural changes.

Regarding the knowledge network as a complex system from a holistic perspective.

Observing the incubation mechanism of emergent scientific breakthroughs from a dynamic evolutionary perspective.

https://doi.org/10.1016/j.ipm.2021.102862

Author(s): Haiyun Xu, Rui Luo, Jos Winnink, Chao Wang, Ehsan Elahi
Organization(s): Shandong University of Technology, Jiangsu Academy of Agricultural Sciences, Leiden University
Source: Information Processing & Management
Year: 2021

Determination of factors driving the genome editing field in the CRISPR era using bibliometrics (full-text)

Over the past two decades, the discovery of CRISPR-Cas immune systems and the repurposing of their effector nucleases as biotechnological tools have revolutionized genome editing. The corresponding work has been captured by 90,000 authors representing 7,600 affiliations in 126 countries, who have published more than 19,000 papers spanning medicine, agriculture, and biotechnology. Here, we use tech mining and an integrated bibliometric and networks framework to investigate the CRISPR literature over three time periods. The analysis identified seminal papers, leading authors, influential journals, and rising applications and topics interconnected through collaborative networks. A core set of foundational topics gave rise to diverging avenues of research and applications, reflecting a bona fide disruptive emerging technology. This analysis illustrates how bibliometrics can identify key factors, decipher rising trends, and untangle emerging applications and technologies that dynamically shape a morphing field, and provides insights into the trajectory of genome editing

For FULL-TEXT go to https://www.liebertpub.com/doi/10.1089/crispr.2021.0001?url_ver=Z39.88-2003&rfr_id=ori%3Arid%3Acrossref.org&rfr_dat=cr_pub++0pubmed

https://doi.org/10.1089/crispr.2021.0001

Author(s): Ying Huang, Yi Zhang, Mengjia Wu, Alan Porter, and Rodolphe Barrangou
Organization(s): Wuhan University, University of Technology Sydney, North Carolina State University
Source: The CRISPR Journal
Year: 2021

A text mining-based approach for the evaluation of patenting trends on nanomaterials

Technological developments in nanomaterials can be tracked using patent indicators. However, the traditional International Patent Classification indicators cannot be considered conclusive, since nanotechnology is not easily defined as a field of research as well as there are different types of nanomaterials not well delineated into hierarchical codes. Therefore, text mining approaches can be used to enhance patent analysis and provide insightful trends to support research and development, competitive intelligence, and policy making. This study aims at proposing a method to classify nanomaterials into main types and mapping technological developments using an advanced text mining-based method to compile patent indicators. Patent records were provided by Derwent Innovations Index database, which indexes an enhanced bibliographic data of patents filed worldwide. A comparison between the IPC indicators and those developed here by text mining is presented. We concluded that the proposed method provides useful outcomes for decision-making, technological forecasting, and material selection process.

https://doi.org/10.1007/s11051-021-05304-3

Author(s): Douglas Henrique Milanez, Leandro Innocentini Lopes de Faria, Daniel Rodrigo Leiva
Organization(s): Federal University of São Carlos
Source: Journal of Nanoparticle Research
Year: 2021

Combining tech mining and semantic TRIZ for technology assessment: Dye-sensitized solar cell as a case (FULL-TEXT)

In a competitive business environment, an early understanding of the dynamics of technological change is crucial to help policymakers and managers make better-informed decisions. Bibliometric analyses help in studying trends and technological evolution. Tech mining (text analyses of science and technology information resources) enhances Bibliometric analyses. However, more often than not, such analyses focus on a specific technological area, and mainly result in incremental advance forecasts. An analysis of the interconnected dynamics of technology change warrants new approaches for identifying technology emergence, technological substitution, and the influences of vital socioeconomic forces. This paper introduces a unique combination that applies a tech mining and semantic TRIZ as a case study to Dye-Sensitized Solar Cell (DSSC) technology. This methodological combination brings broader insights to the emergence of DSSC in conjunction with related technologies that affect its progress, enriching the associated technological progression’s empirical characterization.

  • Techmining-semantic TRIZ helps to understand the competition influence among technologies.
  • The understanding of the architectural design, the system, helps to clearly understand the role of the different components.
  • Understanding the components’ role in the system helps to guide the techmining analysis and to understand the different trends.
  • Using the S-AO and not the SAO problem solving, the present work is able to find other competing or not, technologies that help to understand if that will support the emergence of the original or the competing technology.
  • This cross-tech-components have different role in other architectures. Perovskites, enhance silicon solar cells efficiency.

https://doi.org/10.1016/j.techfore.2021.120826 or download FULL-TEXT

Author(s):J.M. Vicente-Gomila, M.A. Artacho-Ramírez, Ma Ting, A.L.Porter
Organization(s):Universitat Politècnica de València, Beijing Institute of Technology, Search Technology
Source: Technological Forecasting and Social Change
Year: 2021

Corporate Engagement with Nanotechnology through Research Publications (FULL-TEXT)

Assessing corporate engagement with an emerging technology is essential for understanding the development of research and innovation systems. Corporate publishing is used as a system-level knowledge transfer indicator, but prior literature suggests that publishing can run counter to private sector needs for management of dissemination to ensure appropriability of research benefits. We examine the extent of corporate authorship and collaboration in nanotechnology publications from 2000 to 2019. The analysis identified 53,200 corporate nanotechnology publications. Despite the potential for limits on collaboration with corporate authors, this paper finds that eight out of 10 nanotechnology corporate publications involved authors from multiple organizations and nearly one-third from multiple countries and that these percentages were higher in recent years. The USA is the leading nation in corporate nanotechnology publishing, followed by Japan and Germany, with China ranking fourth, albeit with the greatest publication growth rate. US corporate publishing is more highly cited and less cross-nationally collaborative. Asian countries also have fewer collaborative authorship ties outside of their home countries. European countries had more corporate collaborations with authors affiliated with organizations outside of their home countries. The paper concludes that distinguishing corporate publications, while difficult due to challenges in identifying small- and medium-sized corporations and grouping variations in corporate names, can be beneficial to examining national systems of research and development

Link to FULL-TEXT https://www.researchgate.net/publication/350580880_Corporate_engagement_with_nanotechnology_through_research_publications

Author(s): Jan Youtie, Robert Ward, Philip Shapira, Alan L. Porter, Nils Newman
Organizaton(s): Georgia Institute of Technology, Search Technology
Source: Journal of Nanoparticle Research
Year: 2021

Profiling and predicting the problem-solving patterns in China’s research systems: A methodology of intelligent bibliometrics and empirical insights (FULL-TEXT)

Uncovering the driving forces, strategic landscapes, and evolutionary mechanisms of China’s research systems is attracting rising interest around the globe. One such interest is to understand the problem-solving patterns in China’s research systems now and in the future. Targeting a set of high-quality research articles published by Chinese researchers between 2009 and 2018, and indexed in the Essential Science Indicators database, we developed an intelligent bibliometrics-based methodology for identifying the problem-solving patterns from scientific documents. Specifically, science overlay maps incorporating link prediction were used to profile China’s disciplinary interactions and predict potential cross-disciplinary innovation at a macro level. We proposed a function incorporating word embedding techniques to represent subjects, actions, and objects (SAO) retrieved from combined titles and abstracts into vectors and constructed a tri-layer SAO network to visualize SAOs and their semantic relationships. Then, at a micro level, we developed network analytics for identifying problems and solutions from the SAO network, and recommending potential solutions for existing problems. Empirical insights derived from this study provide clues to understand China’s research strengths and the science policies beneath them, along with the key research problems and solutions Chinese researchers are focusing on now and might pursue in the future.

FULL-TEXT available at https://www.mitpressjournals.org/doi/pdf/10.1162/qss_a_00100

Author(s): Yi Zhang, Mengjia Wu, Zhengyin Hu, Robert Ward, Xue Zhang, Alan Porter
Organization(s): University of Technology Sydney, Chengdu Library and Information Centre (CAS), Georgia Institute of Technology
Source: Quantitative Science Studies
Year: 2020

Interdisciplinary knowledge combinations and emerging technological topics: Implications for reducing uncertainties in research evaluation (FULL-TEXT)

This article puts forth a new indicator of emerging technological topics as a tool for addressing challenges inherent in the evaluation of interdisciplinary research. We present this indicator and test its relationship with interdisciplinary and atypical research combinations. We perform this test by using metadata of scientific publications in three domains with different interdisciplinarity challenges: Nano-Enabled Drug Delivery, Synthetic Biology, and Autonomous Vehicles. Our analysis supports the connection between technological emergence and interdisciplinarity and atypicality in knowledge combinations. We further find that the contributions of interdisciplinary and atypical knowledge combinations to addressing emerging technological topics increase or stay constant over time. Implications for policymakers and contributions to the literature on interdisciplinarity and evaluation are provided.

doi.org/10.1093/reseval/rvaa029

Author(s): Seokbeom Kwon, Jan Youtie, and Alan L. Porter
Organization(s): The University of Tokyo, Georgia Institute of Technology
Source: Research Evaluation
Year: 2020