Summary
SciGraph was a search engine tool developed by Springer Nature, the former URL was https://scigraph.springernature.com/explorer. The technology, which was 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.
Type of site | Search engine |
|---|---|
| Created by | Springer Nature |
| Launched | March 2017 |
Development edit
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 edit
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 edit
- ^ "Springer Nature SciGraph". EurekAlert!. Retrieved 2021-10-25.
- ^ 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.
- ^ a b "Springer Nature Uses LOD to Create a Rich Database for Scientists to Work Together". Ontotext. Retrieved 2021-11-04.
- ^ a b 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.
- ^ "SciGraph – Access". 22 December 2017. Retrieved 2021-10-25.
- ^ a b 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.
- ^ 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.
- ^ Light, Ryan; Moody, James (2020). The Oxford Handbook of Social Networks. New York: Oxford University Press. p. 603. ISBN 978-0-19-025176-5.
- ^ 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.
- ^ 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.
- ^ 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.