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| 과학 지도 제작× | 키워드 동시출현 분석× | |
|---|---|---|
| 분야 | 계량서지학 | 계량서지학 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도 | 2000s | 2000s |
| 창시자≠ | Katy Börner, Chaomei Chen, and others | Bibliometric research community |
| 유형 | Method | Method |
| 원전≠ | Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255. DOI ↗ | Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166. DOI ↗ |
| 별칭≠ | knowledge mapping, domain mapping, research landscape visualization | term co-occurrence, keyword network analysis, thematic analysis, term clustering |
| 관련≠ | 5 | 4 |
| 요약≠ | Science mapping is a bibliometric visualization method that creates visual representations of research domains, showing the structure, development, and relationships of scientific fields. Using bibliographic data (citations, keywords, authors, journals), science mapping algorithms generate network diagrams where nodes represent documents, concepts, or authors and edges represent relationships (citation, collaboration, semantic similarity). The resulting maps make invisible intellectual structures visible, enabling researchers to understand field topology, identify emerging areas, and navigate disciplinary landscapes. Pioneered by Börner, Chen, and Boyack in the 2000s, science mapping has become a standard tool in research evaluation and strategic planning. | Keyword co-occurrence analysis is a text mining and bibliometric method that identifies research themes and their relationships by analyzing how frequently terms or keywords appear together in abstracts, titles, or indexed keywords of scientific publications. When two keywords appear together frequently, they are considered co-occurring, indicating a shared thematic or conceptual relationship. This method rapidly reveals the topical structure of a research field without relying on formal classifications, making it particularly useful for detecting emerging research areas and understanding disciplinary boundaries. |
| ScholarGate데이터셋 ↗ |
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