The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale

Problem
This paper addresses the gap in automated technological roadmapping by proposing a framework for extracting and linking scientific contributions from scholarly literature. The authors highlight that scientific advancements are often interdependent, yet existing methods for mapping these relationships are limited. This work is presented as a preprint and has not undergone peer review, indicating that the findings should be interpreted with caution.

Method
The core technical contribution is the development of the Scientific Contribution Graph (SCG), which is a large-scale resource comprising 2 million scientific contributions derived from 230,000 open-access papers. The SCG is constructed with 12.5 million prerequisite edges that connect contributions based on their dependencies. The authors introduce a novel task termed scientific prerequisite prediction, where models are trained to predict which existing technologies can facilitate future discoveries. The evaluation of this task employs a temporally filtered backtesting approach, achieving a mean average precision (MAP) score of 0.48 using contemporary machine learning models. The specific architectures and training compute details are not disclosed, but the results indicate a significant improvement in predictive capabilities over time.

Results
The authors report a MAP score of 0.48 for scientific prerequisite prediction, which demonstrates a notable advancement in the ability to predict technological dependencies. While specific baseline models are not named, the paper suggests that contemporary models have shown rapid improvements in this domain. The results imply that the SCG can serve as a valuable resource for understanding the landscape of scientific contributions and their interdependencies.

Limitations
The authors acknowledge several limitations, including the potential biases in the selection of papers and contributions, which may affect the comprehensiveness of the SCG. They also note that the performance of the models may vary based on the specific domains of the contributions, and the temporal filtering approach may not capture all relevant dependencies. Additionally, the lack of peer review raises questions about the robustness of the findings. An obvious limitation not explicitly mentioned is the reliance on open-access literature, which may exclude significant contributions from paywalled sources, potentially skewing the representation of scientific advancements.

Why it matters
The implications of this work are substantial for downstream research in automated scientific discovery and impact assessment. By providing a structured framework for understanding the interdependencies of scientific contributions, the SCG can facilitate more informed decision-making in research prioritization and funding allocation. Furthermore, the ability to predict future technological advancements based on existing contributions could accelerate innovation and enhance collaboration across disciplines. This resource may also serve as a foundation for further research into the dynamics of scientific progress and the development of more sophisticated AI-driven tools for knowledge discovery.

Authors: Peter A. Jansen
Source: arXiv:2605.15011
URL: https://arxiv.org/abs/2605.15011v1