文章基本信息

标题：Predicting research trends with semantic and neural networks with an application in quantum physics
本地全文：下载
作者：Mario Krenn ; Anton Zeilinger
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：4
页码：1910-1916
DOI：10.1073/pnas.1914370116
出版社：The National Academy of Sciences of the United States of America
摘要：The vast and growing number of publications in all disciplines of science cannot be comprehended by a single human researcher. As a consequence, researchers have to specialize in narrow subdisciplines, which makes it challenging to uncover scientific connections beyond the own field of research. Thus, access to structured knowledge from a large corpus of publications could help push the frontiers of science. Here, we demonstrate a method to build a semantic network from published scientific literature, which we call Sem Net . We use Sem Net to predict future trends in research and to inspire personalized and surprising seeds of ideas in science. We apply it in the discipline of quantum physics, which has seen an unprecedented growth of activity in recent years. In Sem Net , scientific knowledge is represented as an evolving network using the content of 750,000 scientific papers published since 1919. The nodes of the network correspond to physical concepts, and links between two nodes are drawn when two concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside Sem Net , thus confirming that it stores useful semantic knowledge. We train a neural network using states of Sem Net of the past to predict future developments in quantum physics and confirm high-quality predictions using historic data. Using network theoretical tools, we can suggest personalized, out-of-the-box ideas by identifying pairs of concepts, which have unique and extremal semantic network properties. Finally, we consider possible future developments and implications of our findings.
关键词：semantic network ; machine learning ; quantum physics ; metascience ; computer-inspired science