Historical growth of concept networks in Wikipedia

Harang Ju, D. Zhou, A. S. Blevins, D. Lydon‐Staley, Judith R. H. Kaplan, Julio R. Tuma, D. Bassett
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引用次数: 3

Abstract

Philosophers of science have long questioned how collective scientific knowledge grows. Although disparate answers have been posited, empirical validation has been challenging due to limitations in collecting and systematizing large historical records. Here, we introduce new methods to analyze scientific knowledge formulated as a growing network of articles on Wikipedia and their hyperlinks. We demonstrate that in Wikipedia, concept networks in subdisciplines of science do not grow by expanding from their central core to reach an ancillary periphery. Instead, science concept networks in Wikipedia grow by creating and filling knowledge gaps. Notably, the process of gap formation and closure may be valued by the scientific community, as evidenced by the fact that it produces discoveries that are more frequently awarded Nobel prizes than other processes. To determine whether and how the gap process is interrupted by paradigm shifts, we operationalize a paradigm as a particular subdivision of scientific concepts into network modules. Hence, paradigm shifts are reconfigurations of those modules. The approach allows us to identify a temporal signature in structural stability across scientific subjects in Wikipedia. In a network formulation of scientific discovery, our findings suggest that data-driven conditions underlying scientific breakthroughs depend as much on exploring uncharted gaps as on exploiting existing disciplines and support policies that encourage new interdisciplinary research.
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维基百科中概念网络的历史发展
科学哲学家长期以来一直质疑集体科学知识是如何增长的。尽管已经提出了不同的答案,但由于收集和系统化大型历史记录的局限性,经验验证一直具有挑战性。在这里,我们介绍了新的方法来分析科学知识,这些知识是由维基百科上不断增长的文章网络及其超链接形成的。我们证明,在维基百科中,科学分支学科的概念网络不会通过从其核心扩展到辅助边缘而增长。相反,维基百科中的科学概念网络是通过创造和填补知识空白而发展起来的。值得注意的是,裂缝形成和闭合的过程可能受到科学界的重视,这一点可以从它产生的发现比其他过程更频繁地获得诺贝尔奖这一事实中得到证明。为了确定差距过程是否以及如何被范式转换打断,我们将范式作为科学概念的特定细分操作到网络模块中。因此,范式转换是对这些模块的重新配置。该方法使我们能够识别维基百科中科学主题结构稳定性的时间特征。在科学发现的网络表述中,我们的研究结果表明,科学突破背后的数据驱动条件既依赖于探索未知的差距,也依赖于利用现有学科和鼓励新的跨学科研究的支持政策。
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