Semantic Scholar

AI-powered academic search engine that helps researchers efficiently discover, understand, and navigate scientific literature.

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Product Overview

What is Semantic Scholar?

Semantic Scholar, developed by the Allen Institute for AI and launched in 2015, is a free, AI-driven research tool designed to accelerate scientific discovery. It indexes over 200 million academic papers across all scientific fields and uses advanced natural language processing and machine learning to extract meaningful insights, generate concise paper summaries, and identify key connections within the literature. The platform offers features like personalized research feeds, augmented reading experiences via Semantic Reader, and detailed citation analysis to help scholars overcome information overload and focus on the most relevant research.

Key Features

AI-Enhanced Search and Summarization
Utilizes machine learning to extract key points and generate concise summaries of papers, enabling faster comprehension.
Semantic Reader
An augmented reading tool that provides in-line citation cards with TLDR summaries, skimming highlights, and contextual definitions to improve paper reading experience.
Comprehensive Academic Corpus
Indexes over 200 million papers from multiple disciplines, including computer science, biomedicine, and more, sourced from publishers and web crawls.
Personalized Research Feeds and Alerts
Adaptive recommender system that learns user preferences to suggest relevant new papers and allows users to save and organize research libraries.
Rich Citation and Metadata Analysis
Provides detailed citation graphs, author profiles, and paper impact metrics to help users evaluate research significance.

Use Cases

Academic Literature Review : Researchers can quickly find, summarize, and understand relevant scientific papers to support their studies.
Efficient Paper Reading : Students and scholars use Semantic Reader to navigate complex papers with enhanced comprehension tools and contextual insights.
Research Trend Discovery : Scientists identify emerging topics and influential works through AI-driven citation analysis and research feeds.
Author and Institution Profiling : Users track author contributions and institutional research output via automatically generated profiles and bibliographic data.