Mapping the physics research space: a machine learning approach
Mapping the physics research space: a machine learning approach
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DOI：
10.1140/epjds/s13688-019-0210-z
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发表时间：
2019-12-01
期刊：
影响因子：
3.6
通讯作者：
Vespignani, Alessandro
中科院分区：
文献类型：
--
作者：
Chinazzi, Matteo;Goncalves, Bruno;Vespignani, Alessandro
科学发现不是在真空中发生的，而是通过以新的和创造性的方式连接现有知识而发生的。因此，绘制科学知识的关系和结构对于我们理解科学生产的动态至关重要。在这里，我们介绍了一种基于机器学习方法生成科学知识图谱的新方法，该方法从观察到的作者的出版模式开始，生成一个 N 维空间，可以测量不同研究主题和知识领域之间的相似性或距离。我们提供了所提出方法的实现，该方法考虑了美国物理学会出版物数据库，并生成了物理学研究空间的地图，该地图描述了研究主题之间随时间的关系。我们利用这张地图衡量研究能力指纹和知识密度两个指标，来描绘全球400多个城市地区的物理科学研究活动。我们表明，这些指标可用于分析和预测特定地理区域的研究能力和专业化随时间的演变。此外，我们根据我们的方法对 67 个国家的社会经济发展指标与创造新知识的能力之间的关系进行了广泛的分析，强调了科学生产力的一些关键相关性。所提出的方法可扩展到非常大的数据集，并且可以扩展到研究其他学科和研究领域，而无需依赖临时科学分类方案。
Scientific discoveries do not occur in vacuum but rather by connecting existing pieces of knowledge in new and creative ways. Mapping the relation and structure of scientific knowledge is therefore central to our understanding of the dynamics of scientific production. Here we introduce a new approach to generate scientific knowledge maps based on a machine learning approach that, starting from the observed publication patterns of authors, generates an N-dimensional space where it is possible to measure the similarity or distance between different research topics and knowledge domains. We provide an implementation of the proposed approach that considers the American Physical Society publications database and generates a map of the research space in Physics that characterizes the relation among research topics over time. We use this map to measure two indicators, the research capacity fingerprint and the knowledge density, to profile the research activity in physical sciences of more than 400 urban areas across the world. We show that these indicators can be used to analyze and predict the evolution over time of the research capacity and specialization of specific geographical areas. Furthermore we provide an extensive analysis of the relation between socio-economic development indicators and the ability to produce new knowledge for 67 countries, as measured by our approach, highlighting some key correlates of scientific production capacity. The proposed approach is scalable to very large datasets and can be extended to study other disciplines and research areas without having to rely on ad-hoc science classification schemes.