The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
arXiv:2605.15011v2 Announce Type: replace 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 s
The proliferation of AI/NLP research and the increasing need to manage scientific knowledge at scale are driving the development of automated tools for literature analysis.
This development could significantly accelerate scientific discovery and technological roadmapping by making the lineage and dependencies of scientific contributions explicit and searchable.
The ability to automatically map scientific prerequisites and contributions changes how research pathways are identified, potentially improving efficiency in R&D and strategic planning.
- · AI/NLP researchers and developers
- · R&D intensive industries
- · Science funding bodies
- · Governments
- · Manual literature review processes
- · siloed research efforts
Automated identification of key scientific prerequisites will streamline research and development efforts.
This could lead to faster technological advancement by revealing previously obscure connections and reducing redundant research.
Nations and organizations with superior access to and utilization of such 'contribution graphs' could gain a significant strategic advantage in innovation.
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Read at arXiv cs.CL