Unraveling Scientific Evolutionary Paths: An Embedding-Based Topic Analysis

Jin, Q; Chen, H; Zhang, Y; Wang, X; Zhu, D

Unraveling Scientific Evolutionary Paths: An Embedding-Based Topic Analysis

Jin, Q Chen, H Zhang, Y

Wang, X Zhu, D

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Engineering Management, 2023, PP, (99), pp. 1-15
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Jin, Q
dc.contributor.author	Chen, H
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Wang, X
dc.contributor.author	Zhu, D
dc.date.accessioned	2024-02-29T05:36:29Z
dc.date.available	2024-02-29T05:36:29Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Engineering Management, 2023, PP, (99), pp. 1-15
dc.identifier.issn	0018-9391
dc.identifier.issn	1558-0040
dc.identifier.uri	http://hdl.handle.net/10453/175970
dc.description.abstract	Understanding the evolution of knowledge has been and will continue to be the key task of science, technology, and innovation management. Existing research on evolutionary path identification relies primarily on traditional co-occurrence analysis and bag-of-words (BOW)–based models for topic extraction. However, these approaches have limitations in effectively capturing the underlying semantics and linkages of the topics. In this article, we propose a novel embedding-based methodology for scientific evolution analysis, in which word embedding, document embedding, clustering, and network analysis are applied to extract topics, measure topical semantic similarities, and quantitatively distinguish topics’ evolutionary states. We first perform benchmark experiments to demonstrate that doc2vec generally outperforms the BOW-based models in topic extraction before evolution analysis. We then consider topic consistency in vector spaces to identify evolutionary states including newborn, convergence, inheritance, and extinction. Scientific evolutionary paths are finally unraveled based on topic similarity matrixes and evolutionary states. We conduct a case study on object detection research to validate the effectiveness of our methodology. The empirical results, validated by domain experts, demonstrate that the proposed methodology is capable of effectively revealing patterns of knowledge inheritance and integration. Consequently, this methodology can be used to improve decision-making processes in future innovation management.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Engineering Management
dc.relation.isbasedon	10.1109/TEM.2023.3312923
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Business & Management
dc.subject.classification	35 Commerce, management, tourism and services
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Unraveling Scientific Evolutionary Paths: An Embedding-Based Topic Analysis
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	15 Commerce, Management, Tourism and Services
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-10-05T00:00:00+1000Z
dc.date.updated	2024-02-29T05:36:24Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Understanding the evolution of knowledge has been and will continue to be the key task of science, technology, and innovation management. Existing research on evolutionary path identification relies primarily on traditional co-occurrence analysis and bag-of-words (BOW)–based models for topic extraction. However, these approaches have limitations in effectively capturing the underlying semantics and linkages of the topics. In this article, we propose a novel embedding-based methodology for scientific evolution analysis, in which word embedding, document embedding, clustering, and network analysis are applied to extract topics, measure topical semantic similarities, and quantitatively distinguish topics’ evolutionary states. We first perform benchmark experiments to demonstrate that doc2vec generally outperforms the BOW-based models in topic extraction before evolution analysis. We then consider topic consistency in vector spaces to identify evolutionary states including newborn, convergence, inheritance, and extinction. Scientific evolutionary paths are finally unraveled based on topic similarity matrixes and evolutionary states. We conduct a case study on object detection research to validate the effectiveness of our methodology. The empirical results, validated by domain experts, demonstrate that the proposed methodology is capable of effectively revealing patterns of knowledge inheritance and integration. Consequently, this methodology can be used to improve decision-making processes in future innovation management.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/175970