With millions of research papers published every year, there is a huge information overload in scientific literature search. Semantic Scholar leverages our AI expertise to help researchers find the most relevant information efficiently. We utilize methods from data mining, natural-language processing, and computer vision to create powerful new search and discovery experiences. Starting with Computer Science in 2015, we've since scaled the service to all fields of science and are now investing heavily in value-add features in support of AI2’s mission of "AI for the Common Good."
Semantic Scholar makes data about research papers in our corpus freely available through a public API and in bulk through our open research corpus.APIOpen Corpus
Cite-o-matic is a deep learning model for literature review, specifically trained to learn to give meaningful predictions, even when it’s wrong. Cite-o-matic requires only a title and abstract to give useful results, allowing it to be used at any stage in the writing process.RepoPublication
This state-of-the-art tool extracts relevant metadata from PDFs of scholarly articles, including titles, author information, abstracts, sections and references.Repo V1Repo V2
State of the art PDF extraction mechanisms specifically targeted to scholarly articles.
For serving relevant results for queries specific to the academic domain.
A user interface tailored to academic search with features that support the academic community.