Peng Hao, Ke Qing, Budak Ceren, Romero Daniel M, Ahn Yong-Yeol
School of Information, University of Michigan, Ann Arbor, MI 48109, USA.
Center for Complex Network Research, Northeastern University, Boston, MA 02115, USA.
Sci Adv. 2021 Apr 23;7(17). doi: 10.1126/sciadv.abb9004. Print 2021 Apr.
Understanding the structure of knowledge domains is one of the foundational challenges in the science of science. Here, we propose a neural embedding technique that leverages the information contained in the citation network to obtain continuous vector representations of scientific periodicals. We demonstrate that our periodical embeddings encode nuanced relationships between periodicals and the complex disciplinary and interdisciplinary structure of science, allowing us to make cross-disciplinary analogies between periodicals. Furthermore, we show that the embeddings capture meaningful "axes" that encompass knowledge domains, such as an axis from "soft" to "hard" sciences or from "social" to "biological" sciences, which allow us to quantitatively ground periodicals on a given dimension. By offering novel quantification in the science of science, our framework may, in turn, facilitate the study of how knowledge is created and organized.
理解知识领域的结构是科学学中的基础性挑战之一。在此，我们提出一种神经嵌入技术，该技术利用引文网络中包含的信息来获取科学期刊的连续向量表示。我们证明，我们的期刊嵌入编码了期刊之间细微的关系以及科学复杂的学科和跨学科结构，使我们能够在期刊之间进行跨学科类比。此外，我们表明这些嵌入捕捉到了包含知识领域的有意义的“轴”，比如从“软”科学到“硬”科学或从“社会”科学到“生物”科学的轴，这使我们能够在给定维度上对期刊进行定量定位。通过在科学学中提供新颖的量化方法，我们的框架反过来可能会促进对知识如何产生和组织的研究。
