Chavalarias David, Lobbé Quentin, Delanoë Alexandre
CNRS, Complex Systems Institute of Paris Île-de-France (ISC-PIF), 113 rue Nationale, 75013 Paris, France.
EHESS, Centre d'Analyse et de Mathématique Sociales (CAMS), 75006 Paris, France.
Scientometrics. 2022;127(1):545-575. doi: 10.1007/s11192-021-04186-5. Epub 2021 Nov 22.
In 1751, Jean le Rond d'Alembert had a dream: "to make a genealogical or encyclopedic tree which will gather the various branches of knowledge together under a single point of view and will serve to indicate their origin and their relationships to one another". In this paper, we address the question identifying the branches of science by taking advantage of the massive digitization of scientific production. In the framework of complex systems studies, we first formalize the notion of level and scale of knowledge dynamics. Then, we demonstrate how we can reconstruct a reasonably precise and concise multi-scale and multi-level approximation of the dynamical structures of Science: phylomemies. We introduce the notion of phylomemetic networks-projections of phylomemies in low dimensional spaces that can be grasped by the human mind-and propose a new algorithm to reconstruct both phylomemies and the associated phylomemetic networks. This algorithm offers, passing, a new temporal clustering on evolving semantic networks. Last, we show how phylomemy reconstruction can take into account users' preferences within the framework of embodied cognition, thus defining a third way between the quest for objective "ground truth" and the ad-hoc adaptation to a particular user's preferences. The robustness of this approach is illustrated by several case studies.
The online version contains supplementary material available at 10.1007/s11192-021-04186-5.
1751年,让·勒朗·达朗贝尔做了一个梦:“制作一棵谱系树或百科全书式的树,它将把知识的各个分支从单一视角汇聚在一起,并用于表明它们的起源以及相互之间的关系”。在本文中,我们利用科学产出的大规模数字化来解决识别科学分支的问题。在复杂系统研究的框架内,我们首先形式化知识动态的层次和尺度概念。然后,我们展示如何能够重建一个相当精确和简洁的科学动态结构的多尺度和多层次近似:知识谱系。我们引入知识谱系网络的概念——知识谱系在低维空间中的投影,人类思维能够理解这些投影——并提出一种新算法来重建知识谱系以及相关的知识谱系网络。该算法顺便在不断演化的语义网络上提供了一种新的时间聚类方法。最后,我们展示知识谱系重建如何能够在具身认知的框架内考虑用户偏好,从而在追求客观“基本事实”和针对特定用户偏好进行特别调整之间定义了第三条道路。几个案例研究说明了这种方法的稳健性。
在线版本包含可在10.1007/s11192-021-04186-5获取的补充材料。