Knowledge Navigation: Inferring the Interlocking Map of Knowledge from Research Trajectories
CoRR(2024)
摘要
"If I have seen further, it is by standing on the shoulders of giants," Isaac
Newton's renowned statement hints that new knowledge builds upon existing
foundations, which means there exists an interdependent relationship between
knowledge, which, yet uncovered, is implied in the historical development of
scientific systems for hundreds of years. By leveraging natural language
processing techniques, this study introduces an innovative embedding scheme
designed to infer the "knowledge interlocking map." This map, derived from the
research trajectories of millions of scholars, reveals the intricate
connections among knowledge. We validate that the inferred map effectively
delineates disciplinary boundaries and captures the intricate relationships
between diverse concepts. The utility of the interlocking map is showcased
through multiple applications. Firstly, we demonstrated the multi-step analogy
inferences within the knowledge space and the functional connectivity between
concepts in different disciplines. Secondly, we trace the evolution of
knowledge across domains, observing trends such as shifts from "Theoretical" to
"Applied" or "Chemistry" to "Biomedical" along predefined functional
directions. Lastly, by analyzing the high-dimensional knowledge network
structure, we found that knowledge connects each other with shorter global
pathways, and the interdisciplinary knowledge plays a critical role in
accessibility of the global knowledge network. Our framework offers a novel
approach to mining knowledge inheritance pathways in extensive scientific
literature, which is of great significance for understanding scientific
development patterns, tailoring scientific learning trajectories, and
accelerating scientific progress.
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