SciGraph

SciGraph is a search engine tool developed by Springer Nature. The technology, which is considered a Linked Open Data (LOD) platform,[1] collects information that covers the research landscape, which includes research projects, publications, conferences, funding agencies, and others.[2] Key features of the platform include the detailed semantic description of the relationship of information and the visualization of the scholarly domain.

SciGraph
Type of site
Search engine
Created bySpringer Nature
URLscigraph.springernature.com/explorer
LaunchedMarch 2017 (2017-03)

Development

The development of SciGraph began with an initiative to create a platform that will host Springer Nature's entire publication archive, which cover texts published as early as 1815.[3] The number of these resources is reported to be about 13 million.[3] The technology behind the platform was built on earlier Springer Nature projects developed for the purpose of collecting information on the research landscape.[4] The first SciGraph data set was published in February 2017.[4] The platform was launched in March 2017 and significantly expanded with the addition of publications of key partners.[5] The datasets span a broad range of topics, which include computer science, medicine, life sciences, chemistry, engineering, and astronomy, among others.[6] The developers also plan to include citations, patents, and clinical trials in the future.[7]

Technology

SciGraph constitutes 1.5 to 2 billion triples where a triple is formatted as "subject-predicate-object" and could link any subject or concept through a predicate (verb) to another object, demonstrating the type of relationship that exists between them.[8] Its graph structure is used by other academic search engines such as Semantic Scholar. [9]

SciGraph collects data from Springer Nature and its partners from the scholarly domain as well as funders, research projects, conferences, affiliations, and publications.[10] The collected information serves as rich semantic description of how information is related and it also provides a visualization of the scholarly domain.[11] The platform has been considered the only large-scale dataset that reconciles authors' affiliations through the disambiguation and linking with external authoritative datasets according to institutions.[6]

References
  1. "Springer Nature SciGraph". EurekAlert!. Retrieved 2021-10-25.
  2. Rucci, Enzo (2020). Cloud Computing, Big Data & Emerging Topics: 8th Conference, JCC-BD&ET 2020, La Plata, Argentina, September 8-10, 2020, Proceedings. Cham, Switzerland: Springer Nature. p. 86. doi:10.1007/978-3-030-61218-4_6. ISBN 978-3-030-61217-7. ISSN 1865-0929. OCLC 1204142972.
  3. "Springer Nature Uses LOD to Create a Rich Database for Scientists to Work Together". Ontotext. Retrieved 2021-11-04.
  4. Hammond, Tony; Pasin, Michele; Theodoris, Evangelos (2017). Data integration and disintegration: Managing Springer Nature SciGraph with SHACL and OWL (PDF). ISWC (Posters, Demos & Industry Tracks). Kobe, Japan. ISSN 1613-0073. S2CID 45786582. Retrieved October 26, 2021.
  5. "SciGraph – Access". 22 December 2017. Retrieved 2021-10-25.
  6. González-Beltrán, Alejandra; Osborne, Francesco; Peroni, Silvio; Vahdati, Sahar (2018). Semantics, Analytics, Visualization: 3rd International Workshop, SAVE-SD 2017, Perth, Australia, April 3, 2017, and 4th International Workshop, SAVE-SD 2018, Lyon, France, April 24, 2018, Revised Selected Papers. Cham: Springer. p. 64. doi:10.1007/978-3-030-01379-0_5. ISBN 978-3-030-01378-3.
  7. Garcia-Silva, Andres; Gómez-Pérez, José Manuél (1 April 2018). Not Just About Size - A Study on the Role of Distributed Word Representations in the Analysis of Scientific Publications (PDF). Dl4Kgs@Eswc 2018. Heraklion, Greece. pp. 21–32. arXiv:1804.01772. Bibcode:2018arXiv180401772G. ISSN 1613-0073.
  8. Light, Ryan; Moody, James (2020). The Oxford Handbook of Social Networks. New York: Oxford University Press. p. 603. ISBN 978-0-19-025176-5.
  9. 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. arXiv:1912.13080. doi:10.1007/978-3-030-45442-5_31. ISBN 978-3-030-45438-8.
  10. Bespalov, Anton; Michel, Martin C.; Steckler, Thomas (2020). Good Research Practice in Non-Clinical Pharmacology and Biomedicine. Handbook of Experimental Pharmacology. Vol. 257. Cham: Springer Nature. p. 343. doi:10.1007/164_2019_290. ISBN 978-3-030-33655-4. PMID 31691858. S2CID 207902492.
  11. Gayo, Jose Emilio Labra; Prud'hommeaux, Eric; Boneva, Iovka; Kontokostas, Dimitris (2018). "Applications". Validating RDF Data. Synthesis Lectures on Data, Semantics, and Knowledge. Morgan & Claypool Publishers. p. 212. doi:10.1007/978-3-031-79478-0_6. ISBN 978-1-68173-164-3. OCLC 1019932975.
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