
ACM Digital Library
The world’s most comprehensive repository of computing research and citation data.


Semantic Scholar is an AI-powered research tool designed to accelerate scientific discovery. It leverages natural language processing and machine learning to analyze and extract key information from scientific papers. The platform offers an Academic Graph API providing data about authors, papers, citations, venues, and SPECTER2 embeddings, enabling developers to build applications that enhance research workflows. The API offers paper search, recommendations, and downloadable datasets. Semantic Reader augments scientific reading with contextual information. Semantic Scholar indexes over 232 million papers across all fields of science, providing a comprehensive resource for researchers, students, and developers. It facilitates paper discovery, citation analysis, and access to academic literature, promoting innovation in science through open data and accessible tools.
Semantic Scholar is an AI-powered research tool designed to accelerate scientific discovery.
Explore all tools that specialize in citation analysis. This domain focus ensures Semantic Scholar delivers optimized results for this specific requirement.
Provides access to data about authors, papers, citations, venues, and SPECTER2 embeddings. Allows linking directly to semanticscholar.org pages.
Recommends papers similar to a given paper using machine learning algorithms.
Provides downloadable links of datasets in the academic graph in JSON format.
Augments scientific reading by making it more accessible and richly contextual. Uses AI to provide summaries and explanations.
Provides vector representations of scientific papers, capturing semantic relationships between them.
Review the Semantic Scholar API documentation.
Request an API key for higher rate limits.
Choose an API endpoint (Academic Graph, Recommendations, Datasets).
Construct API requests using the base URL and resource path.
Implement error handling for status codes (200, 400, 401, 403, 404, 429, 500).
Parse JSON responses to extract relevant data.
Utilize batch endpoints for large data requests.
Consider downloading full datasets for local querying.
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