Semantic Scholar is a new service for scientific literature search and discovery, focusing on semantics and textual understanding.
This search engine allows users to find key papers about a topic or to produce a list of important citations or results in a given paper. It also serves as a resource and test bed for research in AI.
This search engine unveiled on 2 November by the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington, is working towards an understanding of a paper’s content: “We’re trying to get deep into the papers and be fast and clean and usable,” says Oren Etzioni, chief executive officer of AI2.”No one can keep up with the explosive growth of scientific literature. Which papers are most relevant? Which are considered the highest quality? Is anyone else working on this specific or related problem? Now, researchers can begin to answer these questions in seconds, speeding research and solving big problems faster.”
The product is currently limited to searching about 3 million open-access papers in computer science. But the AI2 team aims to broaden that to other fields within a year.
Using machine reading and vision methods, Semantic Scholar crawls the web, finding all PDFs of publically available scientific papers on computer science topics, extracting both text and diagrams/captions, and indexing it all for future contextual retrieval. Using natural language processing, the system identifies the top papers, extracts filtering information and topics, and sorts by what type of paper and how influential its citations are. It provides the scientist with a simple user interface (optimized for mobile) that maps to academic researchers’ expectations. Filters such as topic, date of publication, author and where published are built in. It includes smart, contextual recommendations for further keyword filtering as well.
Read also: Artificial-intelligence institute launches free science search engine, Nature, November 2, 2015.