Visualization of the Semantic Knowledge Landscape of Editing and Publishing Domain in China
- DOI
- 10.2991/978-94-6463-498-3_7How to use a DOI?
- Keywords
- Editing and Publishing; Knowledge Map; Document Embedding; Evolutionary Analysis
- Abstract
The rapid advancement of information technology is reforming the ecosystem of the editing and publishing industry. Under the demand for building novel publishing patterns, it is critical to map out the research trends and evolutionary trajectories of the field of editing and publishing comprehensively. In this study, drawing on large-scale journal papers, we construct the knowledge landscape of editing and publishing research field in China, by manifold learning algorithm UMAP based on the deep semantic associations between papers learned by Doc2vec, to visualize the static and diachronic structure of this field. Firstly, with the Gaussian kernel density function to characterize the heterogeneity of spatial distribution of papers, we identify core research topics in the editing and publishing domain. Then, by respectively constructing cumulative and sliced dynamic knowledge maps, we find that over the past 40 years, the research scope has continued to expand, evidenced by the emergence of new research topics along the edges of the map, and meanwhile, topics within the field keep merging and fusing. Furthermore, according to the pattern of research hotspot transitions, the development is roughly divided into four stages. These findings offer valuable insights for researchers in the publishing field and scientific policymakers.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shuang Zhang AU - Feifan Liu AU - Li Wang AU - Haoxiang Xia PY - 2024 DA - 2024/08/22 TI - Visualization of the Semantic Knowledge Landscape of Editing and Publishing Domain in China BT - Proceedings of 2023 China Science and Technology Information Resource Management and Service Annual Conference (COINFO 2023) PB - Atlantis Press SP - 64 EP - 74 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-498-3_7 DO - 10.2991/978-94-6463-498-3_7 ID - Zhang2024 ER -