Skip to Content

Semantic Scholar

« Back to Glossary Index

**Semantic Scholar Technology and Features:**
– Provides one-sentence summaries of scientific literature.
– Uses artificial intelligence for abstractive techniques.
– Utilizes machine learning, natural language processing, and machine vision for semantic analysis.
– Features Research Feeds, an AI-powered research recommender.
– Offers Semantic Reader for scientific reading.
– Highlights important elements of papers.
– Identifies hidden connections between research topics.
– Assigns a unique identifier called the Semantic Scholar Corpus ID (S2CID) to each paper.
– Utilizes graph structures like Microsoft Academic Knowledge Graph.

**Semantic Scholar Growth and Partnerships:**
– Corpus included over 40 million papers in 2018.
– Metadata reached over 173 million papers by August 2019.
– Indexed 190 million papers by the end of 2020.
– Reached 7 million users per month by the end of 2020.
– Added 25 million scientific papers in 2020.
– Achieved through new publisher partnerships.
– Significant expansion in database size.
– Collaboration with the University of Chicago Press in 2020.
– Collaboration with various publishers to expand the database.

**Impact on Research Community and Collaboration with Publishers:**
– Provides access to a vast repository of scientific papers.
– Facilitates in-depth research and literature reviews.
– Supports scholars in various disciplines.
– Encourages collaboration and knowledge sharing.
– Enhances the quality of academic work.
– Forge partnerships with publishers to expand the database.
– Increases the diversity of available research.
– Promotes a comprehensive knowledge base.
– Strengthens the academic community’s resources.

**Benefits for Scholars and Researchers:**
– Access to a wider range of scientific literature.
– Enables more comprehensive literature reviews.
– Facilitates cutting-edge research.
– Enhances the depth and quality of academic work.
– Supports the advancement of various research fields.

**Future Prospects of Semantic Scholar:**
– Potential for further expansion.
– Continued partnerships with publishers.
– Likely increase in the database size.
– Anticipated improvements in research capabilities.
– Promises enhanced support for academic endeavors.

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.

« Back to Glossary Index