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| Evolutionary Characteristics and Intensity of Domain Knowledge Structure Integrating Semantic Enhancement and Network Coupling |
| Wang Yuefen1,2, Wang Qi1,3, Tian Yining1,2 |
1.Institute for Big Data Science, Tianjin Normal University, Tianjin 300387
2.Management School, Tianjin Normal University, Tianjin 300387
3.School of Liberal Arts, Huaiyin Normal University, Huai’an 223300 |
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Abstract In this study, we investigated the evolutionary features and intensity of domain knowledge structures by using a vocabulary-oriented knowledge analysis method to explore the characteristics and patterns of science and technology development. Based on large-scale automated keyword extraction, we used the contextual information of keywords to identify their functional roles and align them with standardized knowledge units. In this study, we constructed a domain knowledge structure network by combining semantically rich conceptual associations of knowledge units and the network coupling theory. We also designed relevant indicators of evolutionary features and intensity, and analyzed the short-term and long-term evolutionary trends of domain knowledge structures from temporal and incremental perspectives. An empirical study on functional algorithms in the field of artificial intelligence led to the following conclusions. The knowledge structure of the target domain exhibited small-world and scale-free properties. The differences in knowledge structure between adjacent observation windows were generally slight. As time elapsed and the literature accumulated, the differences increased overall, whereas the growth rate occasionally decreased. The technical solution proposed herein can convert literature information units into knowledge units at low cost and with high efficiency, and can be used to reveal the evolutionary process of both the content and structure of implicit knowledge in academic documents.
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Received: 04 October 2025
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