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| Smap: Visualization of Scientific Knowledge Landscape Based on Document Semantics |
| Zhang Shuang1,2, Liu Feifan1,2, Luo Shuangling3, Xia Haoxiang1,2 |
1.Institute of Systems Engineering, Dalian University of Technology, Dalian 116024
2.Research Center for Big Data and Intelligent Decision-Making, Dalian University of Technology, Dalian 116024
3.School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026 |
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Abstract Given the explosive growth of academic literature, the continuous cross-fusion of knowledge, and the expansion and the increasing complexity of scientific research, widespread attention has been drawn to clearly visualizing the knowledge structure drown in massive amounts of literature as well as grasping development trends. Based on document representation learning and manifold learning algorithms, we suggest a method for constructing a semantic map (Smap). First, Doc2Vec is adopted to capture the high-dimensional semantic features between documents; then, UMAP (uniform manifold approximation and projection) is utilized to perform non-linear dimensionality reduction on the semantic proximity of documents. Finally, the kernel density estimation is employed to characterize the knowledge structure according to the heterogeneity of the document distribution. In the empirical experiments, we cover four scientific domains, ranging from thousands-level to millions-level of documents. Then, we construct an Smap, identify knowledge hierarchical structure, and analyze their dynamic evolution. Furthermore, using the classification system provided by Microsoft Academic Graph (MAG), citation relations, and keywords, we quantify the local purity of the document distribution on Smap and the correlation between the map distance and research distinction to verify the effectiveness of the proposed method. By comparing with controlled experiments, we further demonstrate the significance of the effectiveness of our method. This study expands the current methods of visualization systems in the scientific field and provides an alternative visualization method for scientific and technological information services.
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Received: 06 December 2021
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