论文标题
学术期刊的神经嵌入揭示了复杂的学科组织
Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations
论文作者
论文摘要
了解知识领域的结构是科学科学领域的基础挑战之一。在这里，我们提出了一种神经嵌入技术，该技术利用引用网络中包含的信息获得科学期刊的连续矢量表示。我们证明，我们的期刊嵌入编码期刊之间的细微差别关系以及科学的复杂学科和跨学科结构，从而使我们能够在期刊之间进行跨学科的类比。此外，我们表明嵌入式捕获了包含知识领域的有意义的“轴”，例如从“软”到“硬”科学或从“社会”到“生物学”科学的轴，这使我们能够在给定维度上进行定量地面周期。通过提供科学科学量化的新量化，我们的框架可以促进对知识的创造方式的研究。
Understanding the structure of knowledge domains is one of the foundational challenges in 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 as well as 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 science of science, our framework may in turn facilitate the study of how knowledge is created and organized.
