Semantic Scholar
Semantic Scholar is a project developed at the Allen Institute for Artificial Intelligence, released in November 2015. It is designed to be a "smart" search service for journal articles.[1] The project uses a combination of machine learning, natural language processing, machine vision to add a layer of semantic analysis to the traditional methods of citation analysis.[2] In comparison to Google Scholar and PubMed, it is designed to quickly highlight the most important papers and identify the connections between them.
As of November 2016, the corpus includes 10 million papers from computer science and neuroscience, of which 25% fall into the latter category.[2] The near-term goal is to expand to include all the biomedical sciences by 2017, a total of 20 million papers.[2]
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References[edit]
- ^ "Paul Allen's AI research group unveils program that aims to shake up how we search scientific knowledge. Give it a try.". The Washington Post. Retrieved November 3, 2015.
- ^ a b c Bohannon, John (11 November 2016). "A computer program just ranked the most influential brain scientists of the modern era". sciencemag.org. American Association for the Advancement of Science. Retrieved 12 November 2016.
