Evidence map›Paper›PMID 31415621›Full record

ArticlePloS one2019

Quantitative identification of technological paradigm changes using knowledge persistence.

Changbae Mun, Sejun Yoon, Yongmin Kim, Nagarajan Raghavan, Hyunseok Park

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Changbae MunDepartment of Information Systems, Hanyang University, Seoul, Republic of Korea.
Sejun YoonDepartment of Information Systems, Hanyang University, Seoul, Republic of Korea.
Yongmin KimDepartment of Information Systems, Hanyang University, Seoul, Republic of Korea.
Nagarajan RaghavanEngineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore.
Hyunseok ParkDepartment of Information Systems, Hanyang University, Seoul, Republic of Korea.ORCID 0000-0001-6426-1531

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a method to quantitatively identify the changes of technological paradigm over time. Specifically, the method identifies previous paradigms and predicts future paradigms by analyzing a patent citation-based knowledge network. The technological paradigm can be considered as dominantly important knowledge in a specific period. Therefore, we adopted the knowledge persistence which can quantify technological impact of an invention to recent technologies in a knowledge network. High knowledge persistence patents are dominant or paradigmatic inventions in a specific period and so changes of top knowledge persistence patents over time can show paradigm shifts. Moreover, since knowledge persistence of paradigmatic inventions are increasing dramatically faster than other ordinary inventions, recent patents having similar increasing trends in knowledge persistence with previous paradigms are identified as future paradigm inventions. We conducted an empirical case study using patents related to the genome sequencing technology. The results show that the identified previous paradigms are widely recognized as critical inventions in the domain by other studies and the identified future paradigms are also qualitatively significant inventions as promising technologies.

Indexed as

InventionsKnowledgeKnowledge BasesTechnologyHumans

Identifiers

PMID31415621
PMCPMC6695126

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.