About Semantic Scholar
Semantic Scholar is an artificial-intelligence backed search engine for academic publications developed at the Allen Institute for AI and publicly released in November 2015. It uses advances in natural language processing to provide summaries for 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 began as a database surrounding the topics of computer science, geoscience, and neuroscience. However, in 2017 the system began including biomedical literature in its corpus. As of November 2021, they now include publications from all fields of science.
Semantic Scholar provides one-sentence summary of scientific literature. One of its aims was to address the challenge of reading numerous titles and lengthy abstracts on mobile devices. It also seeks to ensure that the three million scientific papers published yearly reach readers since it is estimated that only half of this literature are ever read.
Artificial intelligence is used to capture the essence of a paper, generating it through an "abstractive" technique. 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, tables, entities, and venues from papers.
In contrast with Google Scholar and PubMed, Semantic Scholar is designed to highlight the most important and influential elements of a paper. The AI technology is designed to identify hidden connections and links between research topics. Like the previously cited search engines, Semantic Scholar also exploits graph structures, which include the Microsoft Academic Knowledge Graph, Springer Nature's SciGraph, and the Semantic Scholar Corpus.
Each paper hosted by Semantic Scholar is assigned a unique identifier called the Semantic Scholar Corpus ID (abbreviated S2CID). The following entry is an 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). doi:10.1093/jtm/taaa021. PMID 32052846. S2CID 211099356.
Semantic Scholar is free to use and unlike similar search engines (i.e. Google Scholar) does not search for material that is behind a paywall.
One study compared the search abilities of Semantic Scholar through a systematic approach, and found the search engine to be 98.88% accurate when attempting to uncover the data. The same study examined other Semantic Scholar functions, including tools to survey metadata as well as several citation tools. (*)
JISI is Indexed on the Semantic Scholar
(*) Wikipedia contributors. (2022, May 24). Semantic Scholar. In Wikipedia, The Free Encyclopedia. Retrieved 14:15, June 14, 2022, from https://en.wikipedia.org/w/index.php?title=Semantic_Scholar&oldid=1089639125