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        <title>The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale</title>
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            <namePart type="given">Sunipa</namePart>
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    <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 resource containing 6 million detailed scientific contributions extracted from 655k open-access papers spanning computer science, medicine, biology, physics, chemistry, and other sciences, and connected by 36 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.</abstract>
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    <identifier type="doi">10.18653/2026.findings-emnlp.1342</identifier>
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%0 Conference Proceedings
%T The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
%A Jansen, Peter
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F jansen-2026-scientific
%X 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 resource containing 6 million detailed scientific contributions extracted from 655k open-access papers spanning computer science, medicine, biology, physics, chemistry, and other sciences, and connected by 36 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.
%R 10.18653/2026.findings-emnlp.1342
%U https://aclanthology.org/2026.findings-emnlp.1342/
%U https://doi.org/10.18653/2026.findings-emnlp.1342
%P 28448-28475

Markdown (Informal)

[The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale](https://aclanthology.org/2026.findings-emnlp.1342/) (Jansen, Findings 2026)

• The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale (Jansen, Findings 2026)

ACL

• Peter Jansen. 2026. The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 28448–28475, Budapest, Hungary. Association for Computational Linguistics.