Integrated Bibliometric–discourse Analysis of Engineering Research (2005–2024)

doi:10.22716/sckt.2025.13.4.011

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2025 Vol.13, Issue 4 Preview Page Next Page

Research Article

Integrated Bibliometric–discourse Analysis of Engineering Research (2005–2024) 공학 연구(2005–2024)의 통합 서지계량–담화 분석: Myoungho Ha¹
하명호¹; ¹Professor, College of General Education, Silla University

¹신라대학교 교양교육원 교수

31 December 2025. pp. 125-134

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Abstract

본 연구는 Web of Science Core Collection에서 추출한 2005–2024년 공학 분야 논문 1,017편(Article/Review, English)을 대상으로, 키워드 기반 과학맵과 Coh-Metrix 담화 지표를 통합하여 공학 연구의 지식 구조와 초록 담화를 함께 분석한다. Author Keywords와 Keywords Plus를 병합한 KW_Merged 필드에서 공출현 네트워크, 중심성–밀도 전략도, 포함지수 기반 테마 진화를 도출하고, 동일 표본 초록에 대해 문장 길이, 결속, 어휘다양도 지표를 산출하였다. 그 결과, 설계·시스템·모형·시각 인지와 연결된 로보틱스 허브, 수술 로보틱스 축, 정렬·정위·성과 축이 공학 연구를 지배하는 세 갈래 구조를 형성하는 것으로 나타났다. 전략도와 테마 진화는 최소침습수술에서 수술 로보틱스로 이어지는 계보 강화와 알고리즘·설계·모형과 성과/정렬 용어 간 결속 심화를 보여 준다. Coh-Metrix 분석에서는 후기(2018–2024)에 문장 길이와 참조 결속이 유의하게 증가하여, 구조 재편이 초록 수준에서 보다 조밀한 정보 제시와 명시적 참조로 실현된다는 점이 확인된다.

This study jointly analyzes the knowledge structure and abstract-level discourse of 1,017 engineering journal articles and reviews (2005–2024; English) retrieved from the Web of Science Core Collection by integrating keyword-based science mapping with Coh-Metrix discourse indices. Author Keywords and Keywords Plus were merged into a single KW_Merged field, from which a co-word network, a centrality–density strategic diagram, and inclusion-index–based thematic evolution were derived, while sentence-length, cohesion, and lexical-diversity indices were computed for the same set of abstracts. The results reveal a three-way structure in which a robotics hub linked to design, system, model, and vision/recognition, a surgical-robotics axis, and an alignment/localization/outcome axis jointly organize engineering research. The strategic diagram and thematic evolution trace a strengthened lineage from minimally invasive surgery to robotic surgery and show increasingly tight couplings between algorithm/design/model terms and outcome/alignment vocabulary. Coh-Metrix analysis further indicates significant late-period (2018–2024) increases in sentence length and referential cohesion, suggesting that structural reorganization is realized in abstracts as denser information packaging and more explicit reference.

Keywords

Bibliometric mapping

Co-word network

Strategic diagram

Thematic evolution

Coh-Metrix

References

M. Callon, J.-P. Courtial, and F. Laville, “Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry”, Scientometrics, Vol. 22, No. 1, pp. 155-205, 1991.
10.1007/BF02019280
M. J. Cobo, A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera, “Science mapping software tools: Review, analysis, and cooperative study”, Journal of the American Society for Information Science and Technology, Vol. 62, No. 7, pp. 1382-1402, 2011.
10.1002/asi.21525
M. J. Cobo, A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera, “A bibliometric method for detecting and visualizing emerging research topics”, Journal of the American Society for Information Science and Technology, Vol. 63, No. 8, pp. 1803-1817, 2012.
10.1002/asi.22688
A. C. Graesser, D. S. McNamara, M. Louwerse, and Z. Cai, “Coh-Metrix: Analysis of text on cohesion and language”, Behavior Research Methods, Vol. 36, No. 2, pp. 193-202, 2004.
10.3758/BF03195564
D. S. McNamara, A. C. Graesser, P. M. McCarthy, and Z. Cai, Automated Evaluation of Text and Discourse with Coh-Metrix. Cambridge, U.K.: Cambridge University Press, 2014.
10.1017/CBO9780511894664
N. J. van Eck and L. Waltman, “How to normalize co-occurrence data? An analysis of some well-known similarity measures”, Journal of the American Society for Information Science and Technology, Vol. 60, No. 8, pp. 1635-1651, 2009.
10.1002/asi.21075
V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks”, Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008, 2008.
10.1088/1742-5468/2008/10/P10008
L. Waltman and N. J. van Eck, “A smart local moving algorithm for large-scale modularity-based community detection”, The European Physical Journal B, Vol. 86, 471, 2013.
10.1140/epjb/e2013-40829-0
M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis”, Journal of Informetrics, Vol. 11, No. 4, pp. 959-975, 2017.
10.1016/j.joi.2017.08.007
P. M. McCarthy and S. Jarvis, “MTLD, vocd-D, and HD-D: A validation study of lexical diversity measures”, Behavior Research Methods, Vol. 42, No. 2, pp. 381-392, 2010.
10.3758/BRM.42.2.381
M. A. Covington and J. D. McFall, “Cutting the Gordian knot: The moving-average type-token ratio (MATTR)”, Journal of Quantitative Linguistics, Vol. 17, No. 2, pp. 94-100, 2010.
10.1080/09296171003643098

Information

Publisher :The Society of Convergence Knowledge
Publisher(Ko) :융복합지식학회
Journal Title :The Society of Convergence Knowledge Transactions
Journal Title(Ko) :융복합지식학회논문지
Volume : 13
No :4
Pages :125-134
DOI :https://doi.org/10.22716/sckt.2025.13.4.011

[1] M. Callon, J.-P. Courtial, and F. Laville, “Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry”, Scientometrics, Vol. 22, No. 1, pp. 155-205, 1991.
10.1007/BF02019280

[2] M. J. Cobo, A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera, “Science mapping software tools: Review, analysis, and cooperative study”, Journal of the American Society for Information Science and Technology, Vol. 62, No. 7, pp. 1382-1402, 2011.
10.1002/asi.21525

[3] M. J. Cobo, A. G. López-Herrera, E. Herrera-Viedma, and F. Herrera, “A bibliometric method for detecting and visualizing emerging research topics”, Journal of the American Society for Information Science and Technology, Vol. 63, No. 8, pp. 1803-1817, 2012.
10.1002/asi.22688

[4] A. C. Graesser, D. S. McNamara, M. Louwerse, and Z. Cai, “Coh-Metrix: Analysis of text on cohesion and language”, Behavior Research Methods, Vol. 36, No. 2, pp. 193-202, 2004.
10.3758/BF03195564

[5] D. S. McNamara, A. C. Graesser, P. M. McCarthy, and Z. Cai, Automated Evaluation of Text and Discourse with Coh-Metrix. Cambridge, U.K.: Cambridge University Press, 2014.
10.1017/CBO9780511894664

[6] N. J. van Eck and L. Waltman, “How to normalize co-occurrence data? An analysis of some well-known similarity measures”, Journal of the American Society for Information Science and Technology, Vol. 60, No. 8, pp. 1635-1651, 2009.
10.1002/asi.21075

[7] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks”, Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008, 2008.
10.1088/1742-5468/2008/10/P10008

[8] L. Waltman and N. J. van Eck, “A smart local moving algorithm for large-scale modularity-based community detection”, The European Physical Journal B, Vol. 86, 471, 2013.
10.1140/epjb/e2013-40829-0

[9] M. Aria and C. Cuccurullo, “bibliometrix: An R-tool for comprehensive science mapping analysis”, Journal of Informetrics, Vol. 11, No. 4, pp. 959-975, 2017.
10.1016/j.joi.2017.08.007

[10] P. M. McCarthy and S. Jarvis, “MTLD, vocd-D, and HD-D: A validation study of lexical diversity measures”, Behavior Research Methods, Vol. 42, No. 2, pp. 381-392, 2010.
10.3758/BRM.42.2.381

[11] M. A. Covington and J. D. McFall, “Cutting the Gordian knot: The moving-average type-token ratio (MATTR)”, Journal of Quantitative Linguistics, Vol. 17, No. 2, pp. 94-100, 2010.
10.1080/09296171003643098

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