Semantic Scholar

Semantic Scholar
Semantic Scholar logo.png
Type of site
Search engine
Created by Allen Institute for Artificial Intelligence
Launched November 2015 (2015-11)

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 academic publications.[1] 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.[2] 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 included more than 40 million papers from computer science and biomedicine.[3] In March 2018, Doug Raymond, who developed machine learning initiatives for the Amazon Alexa platform, was hired to lead the Semantic Scholar project.[4]

As of August 2019, the number of included papers had grown to more than 173 million[5] after the addition of the Microsoft Academic Graph records.[6] Each paper hosted by Semantic Scholar is assigned a unique identifier called the Semantic Scholar Corpus ID (or S2CID for short), for example

Liu, Ying; Gayle, Albert A; Wilder-Smith, Annelies; Rocklöv, Joacim (March 2020). "The reproductive number of COVID-19 is higher compared to SARS coronavirus". Journal of Travel Medicine. 27 (2). S2CID:211099356.

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