Overview
Semantic Scholar is an AI-driven research tool that uses natural language processing and machine learning to deliver semantically relevant scientific papers rather than just keyword matches. It powers features like semantic search, citation graphs, TLDR summaries, and personalized recommendations over a graph of more than 200–225 million papers and billions of citation links.
Pricing
Free
- Core Semantic Scholar search, reading, and recommendation features are completely free to use with no subscriptions or hidden fees.
- Users can sign in to save libraries, organize folders, and set up custom research feeds and alerts at no cost.libguides.
API & Open Data
- Semantic Scholar’s Academic Graph (S2AG) and REST API provide free programmatic access to publication, citation, and author data for developers and researchers.
- Data and APIs are offered under open-access terms aligned with its non-profit mission to accelerate science.
Key Features
- Semantic search & filters – AI-enhanced search understands query meaning, with filters for field, year, venue, open-access PDFs, and more.libguides.
- Citation graphs & influence – Visual citation maps and influence metrics help users trace idea development and find highly cited, foundational papers.
- TLDR summaries & Semantic Reader – AI-generated TLDRs and enhanced reading views surface key contributions, figures, and references quickly.
- Personalized recommendations & feeds – Research feeds and email alerts suggest new papers based on users’ libraries, topics, and reading behavior.
- Open data & API access – Semantic Scholar Academic Graph offers a large-scale open literature graph with 200M+ papers, 80M–105M authors, and billions of citation edges.
Best Use Cases
- Academic researchers & students – Discovering and tracking papers, building reading lists, and exploring related work across disciplines.
- Systematic and scoping reviews – Quickly mapping a field, identifying key authors, venues, and high-impact papers.
- Data-driven literature analyses – Using the S2AG API to analyze citation networks, research trends, and collaboration patterns.
- Libraries & institutions – Integrating Semantic Scholar into guides and teaching materials as a core discovery tool.
- Developers building scholarly tools – Leveraging the free API and open data to build apps for literature review, visualization, or metrics.
Pros
- ✅ Completely free, including API – No paywalls or subscriptions; both the web app and API are free to use.
- ✅ AI-powered relevance and summaries – Semantic search, TLDRs, and Semantic Reader significantly speed up finding and skimming relevant work.
- ✅ Large, cross-disciplinary corpus – Covers over 200–225M papers and billions of citations across natural, physical, and social sciences.
- ✅ Strong for literature discovery – Citation graphs, recommendations, and filters make it easy to identify influential papers and gaps.
Cons
- ❌ Coverage not truly complete – Some fields, venues, and very recent or niche papers may be missing or have incomplete metadata.
- ❌ PDF/full-text availability varies – Many records link to paywalled publisher sites; full-text access depends on open access or institutional subscriptions.
- ❌ Research quality still needs human judgment – AI relevance and TLDRs help discovery, but users must still critically appraise methods and findings.
Official Website
Semantic Scholar – Official AI research tool: https://www.semanticscholar.org
Release Date: Semantic Scholar was launched publicly in November 2015 by the Allen Institute for AI (AI2) and has since expanded from computer science and biomedicine to cover most areas of science.libguides.
Last Updated: December 2025