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Semantic Scholar

AI-powered academic search engine, quickly find relevant papers

About Semantic Scholar

Semantic Scholar is a free academic search engine developed by the Allen Institute for AI, using AI technology to help researchers quickly find relevant papers. It not only provides keyword searches but also understands the semantic content of papers, recommending truly relevant research. It displays paper impact metrics, citation relationships, and research trends. It offers features such as paper abstracts and key chart extraction. Covering multiple fields including computer science and biomedicine, it is a powerful assistant for academic research.

Tags

#research#academic#papers#ai
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Pricing

Completely free

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Key Metrics

2026-09-01 - 2026-09-30
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2026-09-01 - 2026-09-30
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Semantic Scholar Core Features

Semantic Search — Uses natural language processing to understand the meaning of queries, returning more relevant results than keyword-based search.

Paper Recommendations — Provides personalized paper recommendations based on your reading history and citations, helping you discover related research.

Citation Graphs — Visualizes citation networks to show how papers influence each other, making it easy to trace the evolution of ideas.

TLDRs (Too Long; Didn't Read) — Generates concise one-sentence summaries for many papers, allowing quick assessment of relevance.

Influential Citations — Highlights citations that have significantly impacted a paper, helping identify key works in a field.

API Access — Offers a free API for developers to integrate Semantic Scholar data into their own applications.

Research Feeds — Allows users to follow specific topics or authors and receive updates on new papers in their area of interest.

Semantic Scholar Subscription Plan

Free

$0

  • Unlimited searches
  • Access to all core features
  • Personalized recommendations
  • API access with rate limits

FAQ from Semantic Scholar

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