The Semantic Scholar Open Data Platform

TL;DR

Semantic Scholar's open data platform leverages advanced extraction and knowledge graph techniques to create the largest open scientific literature graph, facilitating discovery and understanding in scientific research.

Contribution

This paper introduces the Semantic Scholar Academic Graph, combining diverse data sources with state-of-the-art extraction and semantic features, and details the platform's data processing pipeline and APIs.

Findings

Built the largest open scientific literature graph with 200M+ papers

Integrated advanced semantic features like text parsing and embeddings

Provided comprehensive APIs for data access and analysis

Abstract

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.