Semantic Scholar is an AI-powered research tool developed by the Allen Institute for AI (AI2) that provides free access to a large corpus of scientific papers spanning fields such as computer science, medicine, biology, physics, and more. The platform uses machine learning and natural language processing to surface relevant research, extract key information from papers, and surface connections between studies. Features include semantic search, citation graphs, paper summaries, author profiles, and research feeds tailored to user interests. Semantic Scholar aims to help researchers cut through the volume of published literature by surfacing the most relevant and influential work. It indexes hundreds of millions of papers and provides tools such as TLDR summaries, citation context, and alerts for new publications. The platform is freely accessible without a subscription and offers an API for developers and institutions wishing to integrate its data into their own tools and workflows.
Target audience and deployment
- Solo / Freelancer
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- API
Key features
Use cases
- Discover relevant academic papers
- Understand papers quickly with TLDR summaries
- Track citations and research influence
- Monitor new publications in a research area
- Build research tools via API
- Explore author profiles and publication histories
Best for
- Academic researchers who need to efficiently navigate large volumes of scientific literature
- Graduate students who need to conduct thorough literature reviews
- Developers who need to integrate scientific paper data into research applications
- Scientists who need to track citations and measure research impact