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.

Semantic Scholar是一个旨在帮助学者发现和理解科学文献的开放数据平台，通过使用最先进的学术PDF内容提取和自动知识图构建技术，结合公共和专有数据源，构建了迄今最大的开放科学文献图，包括200多万篇论文，8000多万名作者，550万个论文作者的联系，以及24亿多个引用的联系。

语义学者开放数据平台