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 sub-disciplines, 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 pushing the frontiers of science. Here we demonstrate a method to build a semantic network from published scientific literature, which we call SemNet. We use SemNet to predict future trends in research and to inspire new, 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 SemNet, 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 physical concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside SemNet thus confirm that it stores useful semantic knowledge. We train a deep neural network using states of SemNet of the past, to predict future developments in quantum physics research, and confirm high quality predictions using historic data. With the neural network and theoretical network tools we are able to suggest new, 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.

中文翻译：

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使用语义和神经网络预测研究趋势并在量子物理学中应用

一个人类研究人员无法理解所有科学学科中数量庞大且不断增长的出版物。因此，研究人员必须专注于狭窄的子学科，这使得在自己的研究领域之外发现科学联系变得具有挑战性。因此，从大量出版物中获取结构化知识有助于推动科学前沿。在这里，我们展示了一种从已发表的科学文献中构建语义网络的方法，我们称之为 SemNet。我们使用 SemNet 来预测未来的研究趋势，并激发新的、个性化的和令人惊讶的科学思想种子。我们将其应用于量子物理学学科，近年来该学科的活动出现了前所未有的增长。在 SemNet 中，科学知识使用自 1919 年以来发表的 750,000 篇科学论文的内容表示为一个不断发展的网络。网络的节点对应于物理概念，当研究文章中同时研究两个物理概念时，就会绘制两个节点之间的链接。我们从 SemNet 内部确定了过去有影响力和获奖的研究主题，从而确认它存储了有用的语义知识。我们使用过去的 SemNet 状态训练深度神经网络，以预测量子物理研究的未来发展，并使用历史数据确认高质量的预测。借助神经网络和理论网络工具，我们能够通过识别具有独特和极值语义网络属性的概念对来提出新的、个性化的、开箱即用的想法。最后，