Jump to content

Semantic Scholar

From Wikipedia, the free encyclopedia

This is an old revision of this page, as edited by Wi24rd (talk | contribs) at 16:10, 17 May 2022 (removed Category:Internet search engines which is parent of Category:Scholarly search services using HotCat). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

Semantic Scholar
Type of site
Search engine
Created byAllen Institute for Artificial Intelligence
URLsemanticscholar.org
LaunchedNovember 2015 (2015-11)

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.[1] It uses advances in natural language processing to provide summaries for scholarly papers.[2] The Semantic Scholar team is actively researching the use of artificial-intelligence in natural language processing, machine learning, Human-Computer interaction, and information retrieval.[3]

Semantic Scholar began as a database surrounding the topics of computer science, geoscience, and neuroscience.[4] However, in 2017 the system began including biomedical literature in its corpus.[4] As of November 2021, they now include publications from all fields of science.

Technology

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.[5] 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.[6]

Artificial intelligence is used to capture the essence of a paper, generating it through an "abstractive" technique.[2] 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.[7][8]

In contrast with Google Scholar and PubMed, Semantic Scholar is designed to highlight the most important and influential elements of a paper.[9] The AI technology is designed to identify hidden connections and links between research topics.[10] 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.[11]

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.[12][4]

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.[12] The same study examined other Semantic Scholar functions, including tools to survey metadata as well as several citation tools.[12]

Number of users and publications

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.[13] In March 2018, Doug Raymond, who developed machine learning initiatives for the Amazon Alexa platform, was hired to lead the Semantic Scholar project.[14] As of August 2019, the number of included papers had grown to more than 173 million[15] after the addition of the Microsoft Academic Graph records.[16] In 2020, a partnership between Semantic Scholar and the University of Chicago Press Journals made all articles published under the University of Chicago Press available in the Semantic Scholar corpus.[17] At the end of 2020, Semantic Scholar had indexed 190 million papers.[18]

In 2020, users of Semantic Scholar reached seven million a month.[5]

See also

References

  1. ^ Eunjung Cha, Ariana (3 November 2015). "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. Archived from the original on 6 November 2019. Retrieved November 3, 2015.
  2. ^ a b Hao, Karen (November 18, 2020). "An AI helps you summarize the latest in AI". MIT Technology Review. Retrieved 2021-02-16.{{cite web}}: CS1 maint: url-status (link)
  3. ^ "Semantic Scholar Research". research.semanticscholar.org. Retrieved 2021-11-22.
  4. ^ a b c Fricke, Suzanne (2018-01-12). "Semantic Scholar". Journal of the Medical Library Association. 106 (1): 145–147. doi:10.5195/jmla.2018.280. ISSN 1558-9439. S2CID 45802944.
  5. ^ a b Grad, Peter (November 24, 2020). "AI tool summarizes lengthy papers in a sentence". Tech Xplore. Retrieved 2021-02-16.{{cite news}}: CS1 maint: url-status (link)
  6. ^ "Allen Institute's Semantic Scholar now searches across 175 million academic papers". VentureBeat. 2019-10-23. Retrieved 2021-02-16.
  7. ^ Bohannon, John (11 November 2016). "A computer program just ranked the most influential brain scientists of the modern era". Science. doi:10.1126/science.aal0371. Archived from the original on 29 April 2020. Retrieved 12 November 2016.
  8. ^ Christopher Clark; Santosh Divvala (2016), PDFFigures 2.0: Mining figures from research papers, Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries - JCDL '16, Wikidata Q108172042
  9. ^ "Semantic Scholar". International Journal of Language and Literary Studies. Retrieved 2021-11-09.
  10. ^ Baykoucheva, Svetla (2021). Driving Science Information Discovery in the Digital Age. Chandos Publishing. p. 91. ISBN 978-0-12-823724-3.
  11. ^ Jose, Joemon M.; Yilmaz, Emine; Magalhães, João; Castells, Pablo; Ferro, Nicola; Silva, Mário J.; Martins, Flávio (2020). Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part I. Cham, Switzerland: Springer Nature. p. 254. ISBN 978-3-030-45438-8.
  12. ^ a b c Hannousse, Abdelhakim (2021). "Searching relevant papers for software engineering secondary studies: Semantic Scholar coverage and identification role". IET Software. 15 (1): 126–146. doi:10.1049/sfw2.12011. ISSN 1751-8814. S2CID 234053002.
  13. ^ "AI2 scales up Semantic Scholar search engine to encompass biomedical research". GeekWire. 2017-10-17. Archived from the original on 2018-01-19. Retrieved 2018-01-18.
  14. ^ "Tech Moves: Allen Instititue Hires Amazon Alexa Machine Learning Leader; Microsoft Chairman Takes on New Investor Role; and More". GeekWire. 2018-05-02. Archived from the original on 2018-05-10. Retrieved 2018-05-09.
  15. ^ "Semantic Scholat". Semantic Scholar. Archived from the original on 11 August 2019. Retrieved 11 August 2019.
  16. ^ "AI2 joins forces with Microsoft Research to upgrade search tools for scientific studies". GeekWire. 2018-12-05. Archived from the original on 2019-08-25. Retrieved 2019-08-25.
  17. ^ "The University of Chicago Press joins more than 500 publishers working with Semantic Scholar to improve search and discoverability". RCNi Company Limited. Retrieved 2021-11-22.{{cite web}}: CS1 maint: url-status (link)
  18. ^ Dunn, Adriana (December 14, 2020). "Semantic Scholar Adds 25 Million Scientific Papers in 2020 Through New Publisher Partnerships" (PDF). Semantic Scholar. Retrieved November 22, 2021.
Wikidata has the properties:
Retrieved from "https://en.wikipedia.org/w/index.php?title=Semantic_Scholar&oldid=1088365705"