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By leveraging natural language processing techniques, this study introduces the Scientific Concept Navigator, an embedding\u2010based navigation model to infer the \u201cknowledge pathway\u201d from the research trajectories of millions of scholars. We validate that the learned representations effectively delineate disciplinary boundaries and capture the intricate relationships between diverse concepts. Utility of the navigation space is showcased through multiple applications. Firstly, we demonstrate the multi\u2010step analogy inferences between concepts from various disciplines. Secondly, we formulate the cross\u2010domain conceptual dimensions of knowledge, observing the distributional shifts of 19 disciplines along these conceptual dimensions, including \u201cTheoretical\u201d to \u201cApplied,\u201d and \u201cSocietal\u201d to \u201cEconomic,\u201d highlighting the evolution of functional attributes across diverse domains. Lastly, by analyzing the knowledge network structure, we find that knowledge connects with shorter global pathways, and interdisciplinary concepts play a critical role in enhancing accessibility. Our framework offers a novel approach to mining knowledge inheritance pathways from extensive scientific literature, which is of great significance for understanding scientific progression patterns, tailoring scientific learning trajectories, and accelerating scientific progress.<\/jats:p>","DOI":"10.1002\/asi.25005","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T22:59:48Z","timestamp":1743721188000},"page":"1308-1339","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["<scp>SciConNav<\/scp>: Knowledge navigation through contextual learning of extensive scientific research trajectories"],"prefix":"10.1002","volume":"76","author":[{"given":"Shibing","family":"Xiang","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science Southern University of Science and Technology  Shenzhen Guangdong China"},{"name":"Pengcheng Laboratory  Shenzhen Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Zhongguancun Laboratory  Beijing China"},{"name":"Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, School of Artificial Intelligence Beihang University  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Liu","sequence":"additional","affiliation":[{"name":"LMIB, School of Mathematical Sciences Beihang University  Beijing China"},{"name":"Zhengzhou Aerotropolis Institute of Artificial Intelligence  Zhengzhou Henan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yurui","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science Southern University of Science and Technology  Shenzhen Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaolin","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science Southern University of Science and Technology  Shenzhen Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifang","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science Southern University of Science and Technology  Shenzhen Guangdong China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,4]]},"reference":[{"key":"e_1_2_6_2_1","volume-title":"Third European Conference on Knowledge Management: Trinity College Dublin, Ireland","author":"Abdullah M. 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