The Semantic Scholar Open Data Platform
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature. We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction to build the Semantic Scholar Academic Graph, the largest open scientific literature graph to-date, with 200M+ papers, 80M+ authors, 550M+ paper-authorship edges, and 2.4B+ citation edges. The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings. In this paper, we describe the components of the S2 data processing pipeline and the associated APIs offered by the platform. We will update this living document to reflect changes as we add new data offerings and improve existing services.
View on arXiv@article{kinney2025_2301.10140,
title={ The Semantic Scholar Open Data Platform },
author={ Rodney Kinney and Chloe Anastasiades and Russell Authur and Iz Beltagy and Jonathan Bragg and Alexandra Buraczynski and Isabel Cachola and Stefan Candra and Yoganand Chandrasekhar and Arman Cohan and Miles Crawford and Doug Downey and Jason Dunkelberger and Oren Etzioni and Rob Evans and Sergey Feldman and Joseph Gorney and David Graham and Fangzhou Hu and Regan Huff and Daniel King and Sebastian Kohlmeier and Bailey Kuehl and Michael Langan and Daniel Lin and Haokun Liu and Kyle Lo and Jaron Lochner and Kelsey MacMillan and Tyler Murray and Chris Newell and Smita Rao and Shaurya Rohatgi and Paul Sayre and Zejiang Shen and Amanpreet Singh and Luca Soldaini and Shivashankar Subramanian and Amber Tanaka and Alex D. Wade and Linda Wagner and Lucy Lu Wang and Chris Wilhelm and Caroline Wu and Jiangjiang Yang and Angele Zamarron and Madeleine Van Zuylen and Daniel S. Weld },
journal={arXiv preprint arXiv:2301.10140},
year={ 2025 }
}