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Technological Emergence Demonstrated with VantagePoint's EScore

An evaluation of reproducible approaches for identifying the emergence of technological novelties in scientific
publications is available in Scientometric's rescently published article, "Evaluating technological emergence using text analytics:
two case technologies and three approaches." The selected approaches are term counting technique, VantagePoint's EScore, and Latent Dirichlet Allocation (LDA). The study finds that each method provides a somewhat distinct perspectives on technological emergence, and offers advantages depending on an analyst's objective. EScore provides a holistic view of emergence by considering several parameters, namely term frequency, size, and origin of the research community compared to the term count based method and LDA. 

10th Global TechMining Conference "Call for Papers"

 

The VP Institute together with the Beijing Institute of Technology  is pleased to announce the 10th Global TechMining Conference (GTM2020) will take place August 20th  through 23rd of 2020 in Beijing, China. 

Tech mining is a text-oriented form of “Big Data” analytics, generating practical intelligence from Science, Technology & Innovation (ST&I) information by applying bibliometric and text-mining software (e.g., Derwent Data Analyzer (DDA), VantagePoint) as well as other analytical & visualization applications. Tech mining supports decision making in ST&I management – e.g., competitive technical intelligence, R&D management, research evaluation, and triple helix analysis. “Big Data” brings both opportunities and challenges. ST&I management must stay vigilant of the changing landscape of data and analytic models, methods, and applications used to track the dynamics of science, technology, the economy, and society. The focus of GTM2020 is to engage cross-disciplinary networks of analysts, software specialists, researchers, and managers to address such challenges and advance text-data-driven solutions for ST&I management. Submission will be accepted through March 15, 2020 at http://www.gtmconference.org/submissions/

 

VantagePoint in the "Springer Handbook of Science and Technology Indicators"

The recently released The Springer Handbook of Science and Technology Indicators offers a collection of state-of-the-art contributions on quantitative science and technology research...a field that has benefited greatly with developments in text-analytics, including VantagePoint text-mining software. The chapters below offer particular insights...

Application of Text-Analytics in Quantitative Study of Science and Technology (S Ranaei, A Suominen, A Porter, T Kässi) - "... VantagePoint software was used in an iterative process for keyword consolidation ... Toexamine field-specific topics, keyword co-occurrences based on Pearson correlation between the author-assigned keywords were analyzed in VantagePoint software ...

Post Catch-up Trajectories: Publishing and Patenting Activities of China and Korea (CY Wong, HN Fung) - "... activities. The chapter utilized VantagePoint—a text mining software developed by Search Technology Inc.—to conduct the joint patenting mapping exercise with the focus on the top \(\mathrm{30}\) patent assignees. The mapping ..."

Science Mapping Analysis Software Tools: A Review (JA Moral-Munoz, AG López-Herrera, E Herrera-Viedma) "... On the other hand, it is worth highlighting a commercial option, VantagePoint [7.27]. This is a tool for discovering knowledge in relation to patent and academic databases."

VantagePoint explores relationship between Cognitive Science and Educational Research

 

VantagePoint text-analytic capabilities have been heavily employed in a multi-year research grant from the US National Science Foundation, Directorate for Education and Human Resources on “Exploring the relationship between Cognitive Science and Educational Research.”

Search Technology is pleased to share this listing of select published VantagPoint results.

Measuring Interdisciplinary Research Categories and Knowledge Transfer: A Case Study of Connections between Cognitive Science and Education. Perspectives on Science (2019) https://doi.org/10.1162/posc_a_00317

This is a “bottom-up” paper in the sense that it draws lessons in defining disciplinary categories under study from a series of empirical studies of interdisciplinarity. In particular, we are in the process of studying the interchange of research-based knowledge between Cognitive Science and Educational Research. This has posed a set of design decisions that we believe warrant consideration as others study cross-disciplinary research processes.

What people learn about how people learn:  An analysis of citation behavior and the multidisciplinary flow of knowledge. Research Policy  (2019) https://doi.org/10.1016/j.respol.2019.103835

We explore the contention that the seminal US National Academies consensus report, How People Learn (HPL), played a major role in bridging the flow of knowledge from Cognitive Science to Education. The  results are a caution that, even for a highly regarded multidisciplinary work cited widely by publications from multiple disciplines, its direct influence could be largely disciplinary. Implications for the policy goals of fostering interdisciplinary research and the role of National Academies consensus reports are discussed.

The Relationship between Forward and Backward Diversity in CORE Datasets. Scientometrics (2019)  https://doi.org/10.1007/s11192-019-03163-3.

In this paper we seek to better understand the relationship between forward diversity in the Cognitive Science and Educational Research literature, as well as what we call Border fields (i.e. those fields which exist at the intersection of Cognitive Science and Education Research). We find a clear and convincing relationship between forward and backward diversity in the datasets we study. Among all available explanatory variables, Integration scores claim the strongest correlation in terms of their ability to account for forward diversity. When comparing results from this study to benchmark results from a prior study (using the same indicators) the datasets in this study show a tendency to be both more integrative and diffuse.

Learning about learning: patterns of sharing of research knowledge among Education, Border, and Cognitive Science fields. Scientometrics (2019) https://doi.org/10.1007/s11192-019-03012-3

This study explores the patterns of exchange of research knowledge among Education Research, Cognitive Science, and what we call “Border Fields.” We note five findings—first, over time the percentage of Education Research papers that extensively cite Cognitive Science has increased, but the reverse is not true. Second, a high percentage of Border Field papers extensively cite and are cited by the other fields. Border Field authors’ most cited papers overlap those most cited by Education Research and Cognitive Science. Third, over time the Border Fields have moved closer to Education Research than to Cognitive Science, and their publications increasingly cite, and are cited by, other Border Field publications. Fourth, Education Research is especially strongly represented in the literature published outside those WoS-indexed publications. Fifth, the rough patterns observed among these three fields when using a more restricted dataset drawn from the WoS are similar to those observed with the dataset lying outside the WoS, but Education Research shows a far heavier influence than would be indicated by looking at WoS records alone.

 

Calculating “Emergence Scores” Good for Research, Policy, and for Business

In the recently published "Research addressing emerging technological ideas has greater scientific impact" [Research Policy 48 (9)], authors Seokbeom Kwon, Xiaoyu Liu, et al. demonstrate that directing research into areas of noted technical emergence generates long-term payouts for researchers, policymakers, and technology managers.

Authors employed VantagePoint text-mining software for extensive analysis of over 130,000 scientific publications in representative areas of Materials Science, Biotechnology, and Information/Communication Technology (ICT), extracting the intelligence buried in the "language" of a collective scientific discussion as well as well as the temporal and relational waypoints. Results showed a substantial connection between two bibliometric indicators:
1. Emergence Score – Assessing "terms" extracted from publication records against measures of persistence, novelty, growth, community, and scope to determine which sub-topics within a target domain are robustly "emergent"
2. Citation Analysis - Assessing extent of citations to publications containing "emergent" terms

Results show "the degree to which a paper contains technologically emerging ideas is positively and strongly associated with its future citation impact"...and "a series of tests for validation further support our argument that the greater the extent to which scientific knowledge (a paper) contains emerging ideas, the bigger its scientific impact" within and outside its scientific domain.

These insights into the importance of identifying and understanding areas of technical emergence provide new intelligence for all involved in science, technology & innovation (ST&I) - some examples:
1. Academic Researchers- Early-career researchers can pinpoint promising research sub-topics to pursue.
2. Policymakers - Governmental authorities can improve ST&I funding policies, especially those with goals of cross-domain knowledge dissemination.
3. Technology Managers – Technology firms can tune processes for technological opportunity assessment, especially in sectors where scientific research drives technological innovation.

 

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