🤖 AI Summary
This work proposes an automated method for constructing large-scale technology roadmaps to uncover dependency and evolutionary relationships among scientific contributions. Leveraging advanced natural language processing techniques, the authors extract 2 million scientific contributions from 230,000 open-access papers and build a structured dependency graph comprising 12.5 million prerequisite edges. They further introduce, for the first time, the task of "scientific prerequisite prediction" and demonstrate the feasibility of their approach by achieving a mean average precision (MAP) of 0.48 on this task. This study delivers the first large-scale dependency graph of scientific contributions, establishing a novel paradigm for assessing scientific impact and enabling automated knowledge discovery.
📝 Abstract
Scientific contributions rarely develop in isolation, but instead build upon prior discoveries. We formulate the task of automated technological roadmapping as extracting scientific contributions from scholarly articles and linking them to their prerequisites. We present the Scientific Contribution Graph, a large-scale AI/NLP-domain resource containing 2 million detailed scientific contributions extracted from 230k open-access papers and connected by 12.5 million prerequisite edges. We further introduce scientific prerequisite prediction, a scientific discovery task in which models predict which existing technologies can enable future discoveries, and show that contemporary models are rapidly improving on this task, reaching 0.48 MAP when evaluated using temporally filtered backtesting. We anticipate technological roadmapping resources such as this will support scientific impact assessment and automated scientific discovery.