The exponential growth in scientific publications poses a severe challenge for human researchers. It forces attention to more narrow sub-fields, which makes it challenging to discover new impactful research ideas and collaborations outside one's own field. While there are ways to predict a scientific paper's future citation counts, they need the research to be finished and the paper written, usually assessing impact long after the idea was conceived. Here we show how to predict the impact of onsets of ideas that have never been published by researchers. For that, we developed a large evolving knowledge graph built from more than 21 million scientific papers. It combines a semantic network created from the content of the papers and an impact network created from the historic citations of papers. Using machine learning, we can predict the dynamic of the evolving network into the future with high accuracy, and thereby the impact of new research directions. We envision that the ability to predict the impact of new ideas will be a crucial component of future artificial muses that can inspire new impactful and interesting scientific ideas.

翻译：科学出版物的指数增长对人类研究者构成了严峻挑战。这迫使人们局限于更狭窄的子领域，使得发现自身领域之外的新颖且有影响力的研究思路和合作变得困难。尽管存在预测科学论文未来引用次数的方法，但这些方法通常需要研究完成并撰写论文，往往在想法提出后很久才能评估其影响力。在此，我们展示了如何预测从未被研究者发表过的初始想法的影响力。为此，我们构建了一个基于超过2100万篇科学论文的大型演化知识图谱。该图谱结合了从论文内容中创建的语义网络和从论文历史引用中创建的影响力网络。通过机器学习，我们能够高精度地预测该演化网络未来的动态变化，从而预测新研究方向的影响力。我们设想，预测新想法影响力的能力将成为未来人工智能缪斯的关键组成部分，它能够激发新颖且有影响力的科学灵感。