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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:
- Ying Liu, Albert A Gayle, Annelies Wilder-Smith, Joacim Rocklöv: The reproductive number of COVID-19 is higher compared to SARS coronavirus. In: Journal of Travel Medicine. 27. Jahrgang, Nr. 2, März 2020, S2CID 211099356, doi:10.1093/jtm/taaa021, PMID 32052846, PMC 7074654 (freier Volltext).
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
- Citation analysis – examination of the frequency, patterns, and graphs of citations in documents
- Citation index
- 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
- ↑ Ariana Eunjung Cha: Paul Allen's AI research group unveils program that aims to shake up how we search scientific knowledge. Give it a try. In: The Washington Post 3 November 2015. Abgerufen im November 3, 2015.
- ↑ 2.0 2.1 Karen Hao: An AI helps you summarize the latest in AI. In: MIT Technology Review. 18. November 2020, abgerufen am 16. Februar 2021 (Lua error in Module:Multilingual at line 149: attempt to index field 'data' (a nil value).).
- ↑ Semantic Scholar Research. In: research.semanticscholar.org. Abgerufen am 22. November 2021.
- ↑ 4.0 4.1 4.2
- ↑ 5.0 5.1 Peter Grad: AI tool summarizes lengthy papers in a sentence In: Tech Xplore November 24, 2020. Abgerufen am 16. Februar 2021. (englisch)
- ↑ Allen Institute's Semantic Scholar now searches across 175 million academic papers. In: VentureBeat. 23. Oktober 2019, abgerufen am 16. Februar 2021 (Lua error in Module:Multilingual at line 149: attempt to index field 'data' (a nil value).).
- ↑ John Bohannon: A computer program just ranked the most influential brain scientists of the modern era. In: Science. 11. November 2016, doi:10.1126/science.aal0371 (sciencemag.org [abgerufen am 12. November 2016]).
- ↑ Lua error in Module:Cite_Q at line 13: attempt to index field 'wikibase' (a nil value).
- ↑ Semantic Scholar. In: International Journal of Language and Literary Studies. Abgerufen am 9. November 2021.
- ↑
- ↑
- ↑ 12.0 12.1 12.2
- ↑ AI2 scales up Semantic Scholar search engine to encompass biomedical research In: GeekWire 17. Oktober 2017. Abgerufen am 18. Januar 2018. (amerikanisches Englisch)
- ↑ Tech Moves: Allen Instititue Hires Amazon Alexa Machine Learning Leader; Microsoft Chairman Takes on New Investor Role; and More. GeekWire, 2. Mai 2018, abgerufen am 9. Mai 2018.
- ↑ Semantic Scholat. In: Semantic Scholar. Abgerufen am 11. August 2019.
- ↑ AI2 joins forces with Microsoft Research to upgrade search tools for scientific studies. In: GeekWire. 5. Dezember 2018, abgerufen am 25. August 2019.
- ↑ The University of Chicago Press joins more than 500 publishers working with Semantic Scholar to improve search and discoverability. In: RCNi Company Limited. Abgerufen am 22. November 2021 (Lua error in Module:Multilingual at line 149: attempt to index field 'data' (a nil value).).
- ↑ Adriana Dunn: Semantic Scholar Adds 25 Million Scientific Papers in 2020 Through New Publisher Partnerships In: Semantic Scholar December 14, 2020. Abgerufen im November 22, 2021.
External links
- Lua error in Module:Official_website at line 90: attempt to index field 'wikibase' (a nil value).
Lua error in Module:Authority_control at line 129: attempt to index field 'wikibase' (a nil value).