Semantic Scholar

Semantic Scholar: Academic Search Engine

Discover scholarly papers, journals, and articles with Semantic Scholar, an AI-powered search engine developed by the Allen Institute for Artificial Intelligence.

What is Semantic Scholar?

Semantic Scholar is an academic search engine developed by the Allen Institute for Artificial Intelligence in Seattle. It was launched in November 2015 with the goal of using AI and machine learning to help researchers find the academic papers and journal articles that are most relevant to their work.

Some key features of Semantic Scholar include:

• Search results ranked by relevance using natural language processing and neural networks rather than just keyword matching
• Summarizations of paper abstracts
• Related paper and author recommendations
• Integration of various academic sources beyond just journals like arXiv, JSTOR, PubMed, and more
• Open API for researchers and developers to build on

The goal of Semantic Scholar is to help researchers discover connections in research that may not be found through traditional search alone. By applying AI techniques, it aims to understand the semantics and context behind academic literature to surface the papers most relevant to a researcher's particular interests or lines of study.

Semantic Scholar Features

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