Semantic Scholar

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Semantic Scholar Overview and Features
– Semantic Scholar is a search engine for scientific research papers.
– It provides a one-sentence summary of scientific literature.
– It addresses the challenge of reading numerous titles and lengthy abstracts on mobile devices.
– The project uses artificial intelligence to generate summaries through an abstractive technique.
– Machine learning, natural language processing, and machine vision are used to add semantic analysis to citation analysis.
– Research Feeds is an AI-powered feature that recommends the latest research to users.

Number of Users and Publications
– The Semantic Scholar corpus includes over 40 million papers from computer science and biomedicine.
– The number of included papers metadata has grown to over 173 million after the addition of Microsoft Academic Graph records.
– A partnership with the University of Chicago Press Journals made all articles published under the University of Chicago Press available in the corpus.
– Semantic Scholar had indexed 190 million papers by the end of 2020.
– It reached seven million users per month in 2020.

AI2’s Involvement in Semantic Scholar
– AI2, the Allen Institute for Artificial Intelligence, is involved with Semantic Scholar.
– AI2 has scaled up the Semantic Scholar search engine to include biomedical research.
– In 2018, AI2 joined forces with Microsoft Research to enhance search tools for scientific studies.
– GeekWire has reported on AI2’s collaboration with Semantic Scholar.
– The GeekWire article from 2018 mentions that AI2 hired a machine learning leader from Amazon Alexa.

Impact and Expansion of Semantic Scholar
– Semantic Scholar has a significant impact on the field of scientific research.
– It aims to improve search and discoverability of scientific studies.
– The search engine has been expanded to encompass a wide range of research topics.
– GeekWire has reported on the expansion of Semantic Scholar’s search tools.
– Semantic Scholar has received recognition and partnership from over 500 publishers.

Relevance and Usage of Semantic Scholar
– Semantic Scholar is widely used by researchers and scholars in the scientific community.
– It offers an extensive collection of scientific papers for users to search and explore.
– The search engine’s coverage includes software engineering and biomedical research.
– Semantic Scholar has received positive feedback for its search capabilities and discoverability.
– The addition of new publisher partnerships has significantly increased the number of papers available.

Semantic Scholar (Wikipedia)

Semantic Scholar is a research tool for scientific literature powered by artificial intelligence. It is developed at the Allen Institute for AI and was publicly released in November 2015. Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated summaries of scholarly papers. The Semantic Scholar team is actively researching the use of artificial intelligence in natural language processing, machine learning, human–computer interaction, and information retrieval.

Semantic Scholar
Type of site
Search engine
Created byAllen Institute for Artificial Intelligence
LaunchedNovember 2, 2015; 8 years ago (2015-11-02)

Semantic Scholar began as a database for the topics of computer science, geoscience, and neuroscience. In 2017, the system began including biomedical literature in its corpus. As of September 2022, it includes over 200 million publications from all fields of science.

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