Type of site
|Created by||Allen Institute for Artificial Intelligence|
Semantic Scholar is a project developed at the Allen Institute for Artificial Intelligence. Publicly released in November 2015, it is designed to be an AI-backed search engine for scientific journal articles. The project uses a combination of machine learning, natural language processing, and machine vision to add a layer of semantic analysis to the traditional methods of citation analysis, and to extract relevant figures, entities, and venues from papers. In comparison to Google Scholar and PubMed, Semantic Scholar is designed to highlight the most important and influential papers, and to identify the connections between them.
As of January 2018, following a 2017 project that added biomedical papers and topic summaries, the Semantic Scholar corpus now includes more than 40 million papers from computer science and biomedicine. In March 2018, Doug Raymond, who developed machine learning initiatives for the Amazon Alexa platform, was hired to lead the Semantic Scholar project. As of August 2019, the number of included papers had grown to more than 173 million.
- Citation analysis
- Citation index
- Knowledge extraction
- List of academic databases and search engines
- "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.
- 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.
- "AI2 scales up Semantic Scholar search engine to encompass biomedical research". GeekWire. 2017-10-17. Retrieved 2018-01-18.
- "Tech Moves: Allen Instititue Hires Amazon Alexa Machine Learning Leader; Microsoft Chairman Takes on New Investor Role; and More". GeekWire. 2018-05-02.
- "main page". Semantic Scholar. Retrieved 11 August 2019.
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