Visualization of the Semantic Knowledge Landscape of Editing and Publishing Domain in China

Shuang Zhang, Feifan Liu, Li Wang and Haoxiang Xia ()
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Shuang Zhang: Dalian University of Technology, School of Economics and Management
Feifan Liu: Dalian University of Technology, School of Economics and Management
Li Wang: Institute of Science and Technology Information of China
Haoxiang Xia: Dalian University of Technology, School of Economics and Management

A chapter in Proceedings of 2023 China Science and Technology Information Resource Management and Service Annual Conference (COINFO 2023), 2024, pp 64-74 from Springer

Abstract: 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.

Keywords: Editing and Publishing; Knowledge Map; Document Embedding; Evolutionary Analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-498-3_7

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