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 SemNet We use SemNet 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 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 concepts are concurrently studied in research articles. We identify influential and prize-winning research topics from the past inside SemNet, thus confirming that it stores useful semantic knowledge. We train a neural network using states of SemNet 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.

中文翻译：

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利用语义和神经网络预测研究趋势，并将其应用于量子物理学中。

单个人类研究人员无法理解在所有科学学科中出版的出版物越来越多。结果，研究人员必须专门研究狭窄的子学科，这使得发现超出自己研究领域之外的科学联系变得充满挑战。因此，从大量出版物中获取结构化知识可以帮助推动科学的前沿。在这里，我们演示一种从已发表的科学文献中构建语义网络的方法，我们称其为SemNet。我们使用SemNet来预测研究的未来趋势，并激发科学思想中个性化和令人惊讶的种子。我们将其应用于量子物理学领域，近年来它的活动空前增长。在SemNet中，自1919年以来发表的750,000篇科学论文的内容将科学知识表示为一个不断发展的网络。该网络的节点对应于物理概念，并且在研究文章中同时研究两个概念时会绘制两个节点之间的链接。我们从SemNet中找出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SemNet的状态训练神经网络，以预测量子物理学的未来发展，并使用历史数据确认高质量的预测。使用网络理论工具，我们可以通过识别具有独特和极端语义网络属性的概念对来建议个性化的即用型想法。最后，