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 c...

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Bibliographic Details
Published in:arXiv.org 2023-01
Main Authors: Kinney, Rodney, Anastasiades, Chloe, Russell Authur, Beltagy, Iz, Bragg, Jonathan, Buraczynski, Alexandra, Cachola, Isabel, Candra, Stefan, Chandrasekhar, Yoganand, Cohan, Arman, Crawford, Miles, Downey, Doug, Dunkelberger, Jason, Etzioni, Oren, Evans, Rob, Feldman, Sergey, Gorney, Joseph, Graham, David, Hu, Fangzhou, Huff, Regan, King, Daniel, Kohlmeier, Sebastian, Kuehl, Bailey, Langan, Michael, Lin, Daniel, Liu, Haokun, Lo, Kyle, Lochner, Jaron, MacMillan, Kelsey, Murray, Tyler, Newell, Chris, Rao, Smita, Rohatgi, Shaurya, Sayre, Paul, Shen, Zejiang, Singh, Amanpreet, Soldaini, Luca, Subramanian, Shivashankar, Tanaka, Amber, Wade, Alex D, Wagner, Linda, Lucy Lu Wang, Wilhelm, Chris, Wu, Caroline, Yang, Jiangjiang, Zamarron, Angele, Madeleine Van Zuylen, Weld, Daniel S
Format: Article
Language:English
Subjects:
Data processing
Graph theory
Knowledge representation
Natural language processing
Open data
Semantics
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Summary: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.
ISSN:2331-8422