Studying the linkage patterns and incremental evolution of domain knowledge structure: a perspective of structure deconstruction

Qi Wang, Bentao Zou, Jialin Jin and Yuefen Wang ()
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Qi Wang: Nanjing University of Science & Technology
Bentao Zou: Nanjing University of Science & Technology
Jialin Jin: North China University of Water Resources and Electric Power
Yuefen Wang: Nanjing University of Science & Technology

Scientometrics, 2024, vol. 129, issue 7, No 22, 4249-4274

Abstract: Abstract The knowledge structure is the result of continuous evolution of the forces intertwined with knowledge linkages and structural patterns. However, the dynamics along this path are still not fully understood. This study aims to investigate the linkage patterns and incremental evolution of domain knowledge structure from the perspective of structure deconstruction. To this end, we proposed a novel framework that integrates the incremental update mechanism of knowledge network construction, subgraph enumeration, and knowledge combination. The proposed integrative framework enables us to embed time-related node attributes into identified subgraphs and to deconstruct specific types of decomposable structure into exiting knowledge combinations and potential knowledge combinations. Results from our case studies, the IIoT and the Metaverse fields, confirmed that the proposed framework is applicable to reveal the underlying knowledge linkage patterns and relative evolution strength. The identified decomposable structures suggest that the path toward knowledge linkages mainly follows a mixed strategy (e.g., high impact knowledge elements are more likely to be linked with elements of middle/low level of impact). The framework designed in this study, together with findings from two fields, elucidates specific evolutionary dynamics through a combined analysis of motifs and structural deconstruction. These findings hold implications for practitioners and policymakers seeking to develop a nuanced understanding of the field.

Keywords: Knowledge structure; Dynamic keywords co-occurrence networks; Decomposable structures; Linkage patterns (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11192-024-05059-3

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