The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.

矛盾的是，在线知识的激增使人们很难找到知识。Web或期刊搜索可能会检索成千上万的文章，其排名方式受（例如）受欢迎程度或特征值中心性而不是与复杂查询的明智相关性所偏颇。每年都有成千上万的文章发表，浓密而纠结的知识丛变得更加交织在一起。尽管自然语言处理和生成知识图谱的新方法可以从研究文章中提取越来越多的高级解释，但结果不可避免地偏向于近期，流行和/或享有盛誉的资源。这是人类社会学习过程固有的结果。为了保存甚至重新发现丢失的科学思想，我们采用的理论是，科学进步是在新范式起源时通过启发性的，革命性的思想来实现的。我们以一个简短的案例为例，提出系统进化论可以用来重新发现潜在有用的丢失发现的方法，以此作为机器可以帮助推动革命性科学发展的一种方式。