Semantic Scholar

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 plan to scale the service to additional scientific areas over the next few years in support of AI2’s mission of “AI for the Common Good”.

Project features currently under development are:

Ability to provide an overview or quickly find the most relevant survey papers for a topic.

Filtering of search results using automatically generated facets like authors and venues.

Identifying “key” citations to overcome citation overload.

Extracting and making figures and captions more easily accessible.

Identifying and presenting useful concepts and their relationships.

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