Semantic Scholar

Semantic Scholar is an artificial intelligence–powered research tool for scientific literature 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
Type of site
Search engine
Created byAllen Institute for Artificial Intelligence
URLsemanticscholar.org
LaunchedNovember 2015 (2015-11)

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 September 2022, they now include over 200 million publications from all fields of science.[5]

Technology

Semantic Scholar provides a 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.[6] 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.[7]

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.[8][9]

In contrast with Google Scholar and PubMed, Semantic Scholar is designed to highlight the most important and influential elements of a paper.[10] The AI technology is designed to identify hidden connections and links between research topics.[11] 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.[12]

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.[13][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.[13] The same study examined other Semantic Scholar functions, including tools to survey metadata as well as several citation tools.[13]

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.[14] In March 2018, Doug Raymond, who developed machine learning initiatives for the Amazon Alexa platform, was hired to lead the Semantic Scholar project.[15] As of August 2019, the number of included papers metadata (not the actual PDFs) had grown to more than 173 million[16] after the addition of the Microsoft Academic Graph records.[17] 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.[18] At the end of 2020, Semantic Scholar had indexed 190 million papers.[19]

In 2020, users of Semantic Scholar reached seven million a month.[6]

See also
  • Citation analysis – Examination of the frequency, patterns, and graphs of citations in documents
  • Citation index – Index of citations between publications
  • Knowledge extraction – Creation of knowledge from structured and unstructured sources
  • List of academic databases and search engines
  • Scientometrics – Study of measuring and analysing science, technology and innovation

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. Hao, Karen (November 18, 2020). "An AI helps you summarize the latest in AI". MIT Technology Review. Retrieved 2021-02-16.
  3. "Semantic Scholar Research". research.semanticscholar.org. Retrieved 2021-11-22.
  4. 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. Matthews, David (1 September 2021). "Drowning in the literature? These smart software tools can help". Nature. Retrieved 5 September 2022. ...the publicly available corpus compiled by Semantic Scholar — a tool set up in 2015 by the Allen Institute for Artificial Intelligence in Seattle, Washington — amounting to around 200 million articles, including preprints.
  6. Grad, Peter (November 24, 2020). "AI tool summarizes lengthy papers in a sentence". Tech Xplore. Retrieved 2021-02-16.
  7. "Allen Institute's Semantic Scholar now searches across 175 million academic papers". VentureBeat. 2019-10-23. Retrieved 2021-02-16.
  8. 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.
  9. Christopher Clark; Santosh Divvala (2016). PDFFigures 2.0: Mining figures from research papers. Proceedings of the 16th ACM/IEEE-CS Joint Conference on Digital Libraries. ISBN 978-1-4503-4229-2. Wikidata Q108172042.
  10. "Semantic Scholar". International Journal of Language and Literary Studies. Retrieved 2021-11-09.
  11. Baykoucheva, Svetla (2021). Driving Science Information Discovery in the Digital Age. Chandos Publishing. p. 91. ISBN 978-0-12-823724-3.
  12. 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.
  13. 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.
  14. "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.
  15. "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.
  16. "Semantic Scholar". Semantic Scholar. Archived from the original on 11 August 2019. Retrieved 11 August 2019.
  17. "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.
  18. "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.
  19. 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.
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