FAIR and interactive data graphics from a scientific knowledge graph

Posted on June 17, 2024 By LabLynx Journal articles

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Ontotext AD
Company typePrivate corporation
IndustrySoftware

Semantic Web
Semantic technology
Linked Data
Text mining
Information discovery
Graph database
Knowledge Engineering Triplestore

Knowledge Graph
Founded2000
Headquarters
Bulgaria Edit this on Wikidata
Key people
Atanas Kiryakov, CEO

Vassil Momtchev, CTO

Veska Davidova, COO
ProductsOntotext GraphDB,[1]

Ontotext Semantic Platform, GraphDB Cloud,[2] Media & Publishing, Marketing Intelligence, Life Sciences & Healthcare, Compliance & Document Management,

Galleries, Libraries, Archives & Museums (GLAM)
WebsiteOntotext web site

Ontotext is a software company that produces software relating to data management. Its main products are GraphDB, an RDF database; and Ontotext Platform, a general data management platform based on knowledge graphs. It was founded in 2000 in Bulgaria, and now has offices internationally.[3] Together with the BBC, Ontotext developed one of the early large-scale industrial semantic applications, Dynamic Semantic Publishing, starting in 2010.[4]

Ontotext GraphDB, formerly OWLIM, is an RDF triplestore optimized for metadata and master data management, as well as graph analytics and data publishing. Since version 8.0 GraphDB integrates OpenRefine to allow for easy ingestion and reconciliation of tabular data.[5] Ontotext Platform is a general-purpose data management tool centered around the idea of knowledge graphs.[3]

Ontotext GraphDB

Ontotext GraphDB (previously known as BigOWLIM) is a graph-based database[6] capable of working with knowledge graphs[7] produced by Ontotext, compliant with the RDF graph data model[8] and the SPARQL query language.[9] Some categorize it as a NoSQL database, meaning that it does not use tables like some other databases.[10] In 2014 Ontotext acquired the trademark "GraphDB" from Sones.[citation needed]

GraphDB is also an advanced ontology (specification of entities, their properties, and their relationships) repository.[11] The underlying idea of the database is of a semantic repository, storing semantic relationships between objects.[12]

Architecture

GraphDB is used to store and manage semantic knowledge graph data.[7] It is built on top of the RDF4J architecture for handling RDF data, implemented through the use of RDF4J's Storage and Inference Layer (SAIL).[citation needed] The architecture is made of three main components:[citation needed]

  • The Workbench is a web-based administration tool. The user interface is based on RDF4J Workbench Web Application.
  • The Engine consists of a query optimizer, reasoner,[13] and a storage and plugin manager.[citation needed] The reasoner in GraphDB is forward chaining, reasoning forward from given priors, with the goal of total materialization.[12] The plugin manager supports user-defined indexes and can be configured dynamically during run-time.[citation needed]

Uses

Ontotext Graph DB has been used in genetics,[14] healthcare,[15] data forensics,[16] cultural heritage studies,[17] geography,[18] infrastructure planning,[19] civil engineering,[20] digital historiography,[21] and oceanography.[22] Commercial clients include the BBC,[23] the Financial Times,[24] Springer Nature,[25] the UK Parliament,[26][27] and AstraZeneca.[23]

See also

References

  1. ^ "Ontotext GraphDB". Ontotext.
  2. ^ "GraphDB Cloud". Ontotext.
  3. ^ a b "Ontotext". Bloor Research. 31 January 2024. Retrieved 25 June 2024.
  4. ^ O'Donovan, John. "The World Cup and a call to action around Linked Data". BBC Internet Blog. Retrieved 2 November 2016.
  5. ^ Howard, Philip (10 May 2017). "Graph update: Ontotext GraphDB". Bloor Research.
  6. ^ "Graph Databases (Technology)". Bloor Research. Retrieved 9 December 2020. We would argue that the market leaders in this space continue to be Neo4J and OntoText (GraphDB), which are graph and RDF database providers respectively.
  7. ^ a b Buchmann, Robert (2019). "Model-Aware Software EngineeringA Knowledge-based Approach to Model-Driven Software Engineering" (PDF). Retrieved 15 April 2021.
  8. ^ "About GraphDB". GraphDB Free 8.4 documentation. Retrieved 25 June 2024.
  9. ^ "SparqlImplementations - W3C Wiki". www.w3.org. Retrieved 15 April 2021.
  10. ^ "GraphDB". Capterra. Retrieved 9 December 2020.
  11. ^ Ledvinka, Martin (2015). "JOPA: Accessing Ontologies in an Object-oriented Way" (PDF). Retrieved 15 April 2021.
  12. ^ a b Kiryakov, Atanas; Ognyanov, Damyan; Manov, Dimitar (2005). "OWLIM – A Pragmatic Semantic Repository for OWL". In Dean, Mike; Guo, Yuanbo; Jun, Woochun; Kaschek, Roland; Krishnaswamy, Shonali; Pan, Zhengxiang; Sheng, Quan Z. (eds.). Web Information Systems Engineering – WISE 2005 Workshops. Lecture Notes in Computer Science. Vol. 3807. Berlin, Heidelberg: Springer. pp. 182–192. doi:10.1007/11581116_19. ISBN 978-3-540-32287-0.
  13. ^ Stoilos, Giorgos; Grau, Bernardo Cuenca; Horrocks, Ian (5 July 2010). "How Incomplete is Your Semantic Web Reasoner?". Proceedings of the AAAI Conference on Artificial Intelligence. 24 (1): 1431–1436. doi:10.1609/aaai.v24i1.7498. ISSN 2374-3468. S2CID 34119609.
  14. ^ Poncheewin, Wasin; Hermes, Gerben D. A.; van Dam, Jesse C. J.; Koehorst, Jasper J.; Smidt, Hauke; Schaap, Peter J. (2020). "NG-Tax 2.0: A Semantic Framework for High-Throughput Amplicon Analysis". Frontiers in Genetics. 10: 1366. doi:10.3389/fgene.2019.01366. ISSN 1664-8021. PMC 6989550. PMID 32117417.
  15. ^ Barisevičius, Gintaras; Coste, Martin; Geleta, David; Juric, Damir; Khodadadi, Mohammad; Stoilos, Giorgos; Zaihrayeu, Ilya (2018). "Supporting Digital Healthcare Services Using Semantic Web Technologies". In Vrandečić, Denny; Bontcheva, Kalina; Suárez-Figueroa, Mari Carmen; Presutti, Valentina; Celino, Irene; Sabou, Marta; Kaffee, Lucie-Aimée; Simperl, Elena (eds.). The Semantic Web – ISWC 2018. Lecture Notes in Computer Science. Vol. 11137. Cham: Springer International Publishing. pp. 291–306. doi:10.1007/978-3-030-00668-6_18. ISBN 978-3-030-00668-6.
  16. ^ Zhuhadar, Leyla; Ciampa, Mark (1 March 2019). "Leveraging learning innovations in cognitive computing with massive data sets: Using the offshore Panama papers leak to discover patterns". Computers in Human Behavior. 92: 507–518. doi:10.1016/j.chb.2017.12.013. ISSN 0747-5632. S2CID 59528294.
  17. ^ Damiano, Rossana; Lombardo, Vincenzo; Lieto, Antonio; Borra, Davide (1 July 2016). "Exploring cultural heritage repositories with creative intelligence. The Labyrinth 3D system". Entertainment Computing. 16: 41–52. doi:10.1016/j.entcom.2016.05.002. hdl:2318/1578514. ISSN 1875-9521. S2CID 31774697.
  18. ^ Panasiuk, Oleksandra (2019). "Representing GeoData for Tourism with Schema.org" (PDF). Retrieved 15 April 2021.
  19. ^ Azzam, Amr; Aryan, Peb Ruswono; Cecconi, Alessio; Di Ciccio, Claudio; Ekaputra, Fajar J.; Fernandez Garcia, Javier David; Karampatakis, Sotiris; Kiesling, Elmar; Musil, Angelika (2019), Antonella Longo, Maria Fazio (ed.), The CitySPIN Platform: A CPSS Environment for City-Wide Infrastructures (PDF), Bilbao, Spain: CEUR Workshop Proceedings, pp. 57–64, retrieved 15 April 2021
  20. ^ Nundloll, Vatsala; Lamb, Rob; Hankin, Barry; Blair, Gordon (1 April 2021). "A semantic approach to enable data integration for the domain of flood risk management". Environmental Challenges. 3: 100064. Bibcode:2021EnvCh...300064N. doi:10.1016/j.envc.2021.100064. ISSN 2667-0100.
  21. ^ Quaresma, Paulo (2020). "Information Extraction from Historical Texts:a Case Study" (PDF). Retrieved 15 April 2021.
  22. ^ Zárate, Marcos; Rosales, Pablo; Braun, Germán; Lewis, Mirtha; Fillottrani, Pablo Rubén; Delrieux, Claudio (2019). "OceanGraph: Some Initial Steps Toward a Oceanographic Knowledge Graph". In Villazón-Terrazas, Boris; Hidalgo-Delgado, Yusniel (eds.). Knowledge Graphs and Semantic Web. Communications in Computer and Information Science. Vol. 1029. Cham: Springer International Publishing. pp. 33–40. doi:10.1007/978-3-030-21395-4_3. ISBN 978-3-030-21395-4. S2CID 160011396.
  23. ^ a b Anadiotis, George. "Graph databases and RDF: It's a family affair". ZDNet. Retrieved 9 December 2020.
  24. ^ "Semantic Technology for online, broadcast and print media". videolectures.net. Retrieved 9 December 2020.
  25. ^ "SciGraph | For Researchers". Springer Nature. Retrieved 9 December 2020.
  26. ^ "Linked Government Data". nationalarchives.gov.uk. Retrieved 9 December 2020.
  27. ^ "Performance testing a graph database | Parliamentary Digital Service". pds.blog.parliament.uk. Retrieved 15 April 2021.
source: https://en.wikipedia.org/wiki/Ontotext_GraphDB

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