Wikipedia

Ontotext GraphDB

Ontotext GraphDB (previously known as BigOWLIM) is a graph database[2][3] and knowledge discovery[4][5][6] tool compliant with RDF[7] and SPARQL[8] and available as a high-availability cluster. Ontotext GraphDB is used in various European research projects.[9]

GraphDB
Developer(s)Ontotext
Stable release
9.9.1 / September 2021 (2021-09) [1]
Repository
Operating systemCross-platform
Available inEnglish
TypeDatabase, Triplestore, Graph databases
LicenseGraphDB-Free is free to use. SE and EE are licensed per CPU-Core used. Perpetual and annual subscription models are available.
Websitewww.ontotext.com/products/graphdb

As of April 2021, Graph DB is ranked as the 4th most -popular[10] RDF store[11][12] and 6th most-popular Graph DBMS system.[13] Some categorize it as a NoSQL database.[14] In 2014 Ontotext acquired the trademark "GraphDB" from Sones.

As for a typical graph DB, ontologies are an important input for the databases.[15] The underlying idea is a semantic repository.[16]

Architecture edit

GraphDB is used to store and manage semantic Knowledge Graph data. It is built on top of the RDF4J architecture implemented through RDF4J's Storage and Inference Layer (SAIL). The architecture is made of three main components:

  • 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,[17] storage and plugin manager. The reasoner in GraphDB is Forward chaining with the goal of total materialization.[18] The plugin manager supports user-defined indexes and can be configured dynamically during run-time. These include:
    • RDF Rank, which is an algorithm that identifies the most relevant entities, similar to Google's PageRank by evaluating their interconnectedness
    • GeoSPARQL, which is the standard for geographical linked data. The plugin is able to convert between coordinate reference systems into the default, which OGC specifies as CRS84 format
    • Lucene, which supports full-text search capabilities. This provides a variety of indexing options and the ability to simultaneously use multiple, differently configured indexes in the same query using Apache Lucene, a high-performance, full-featured text search engine
  • The Connectors: The performance of search such as full-text search and faceted search can be vastly improved via the connectors by enabling the implementation by an external component or service. GraphDB has a connector for both well-known open-source search engines, Solr and Elasticsearch.
    • There is also a connector enabling MongoDB integration, providing the scalability and performance advantages.
    • Relational data virtualization (Ontology-Based Data Access, OBDA) is provided by integration of ontop
    • SQL Access over JDBC is provided[19] for traditional analytics tools such as Tableau and PowerBI
    • Kafka Sink Connector[20] for ingesting large amounts of data.
    • GraphQL access to knowledge graphs[21] and semantic search[22] based on Elasticsearch and exposed through GraphQL.

Features and Integrations edit

According to Ontotext, Graph DB supports:

  • GraphDB uses RDF4J as a library, utilizing its APIs for storage and querying.
  • It supports the GraphQL, SPARQL and SeRQL languages and RDF (e.g., RDF/XML, N3, Turtle) serialization formats.
  • It supports custom reasoning rulesets, as well as RDFS, RDFS-plus, OWL 2 RL and QL.[23]
  • It integrates OpenRefine for the ingestion of tabular data[24] and provides semantic similarity search at the document level.[25]

Uses edit

Ontotext Graph DB is used in various scientific areas, e.g., Genetics,[26] Healthcare,[27] Data Forensics,[28] Cultural Heritage,[29] Geography,[30] Infrastructure Planning,[31] Civil Engineering,[32] Digital Historiography,[33] Oceanography.[34]

For more examples see "Diverse Uses of a Semantic Graph Database for Knowledge Organization and Research" below.

Commercial clients include BBC Sport,[35][36] Financial Times,[37] Springer Nature,[38] UK Parliament,[39][40] AstraZeneca[41] as well as in the pharmaceutical and finance industries.

Some use cases focus on scalability and large data sizes.[42]

See also edit

External links edit

References edit

  1. ^ "Graph Databases (Technology)". Retrieved 2021-10-02.
  2. ^ "Graph Databases (Technology)". Bloor Research. Retrieved 2020-12-09.
  3. ^ "Global Graph Database Market by Type, Application, Component, Deployment Type, Industry Vertical & Region - Analysis & Forecast to 2023 - ResearchAndMarkets.com". businesswire.com. 2018-06-28. Retrieved 2020-12-09.
  4. ^ "KMWorld AI 50: The Companies Empowering Intelligent Knowledge Management". kmworld.com. Retrieved 2020-12-09.
  5. ^ "Global Semantic Knowledge Discovery Software Market Growth (Status and Outlook) 2019-2024 - Market Research Insights". mrinsights.biz. Retrieved 2020-12-09.
  6. ^ Buchmann, Robert (2019). "Model-Aware Software EngineeringA Knowledge-based Approach to Model-Driven Software Engineering" (PDF). Retrieved 2021-04-15.
  7. ^ Motik, Boris; Nenov, Yavor; Piro, Robert; Horrocks, Ian; Olteanu, Dan (2014-06-19). "Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems". Proceedings of the AAAI Conference on Artificial Intelligence. 28 (1). doi:10.1609/aaai.v28i1.8730. ISSN 2374-3468. S2CID 5547916.
  8. ^ "SparqlImplementations - W3C Wiki". www.w3.org. Retrieved 2021-04-15.
  9. ^ "Google Scholar". scholar.google.com. Retrieved 2020-12-09.
  10. ^ "DB-Engines Ranking". DB-Engines. Retrieved 2020-12-09.
  11. ^ Guest, CIO Central. "The Hype Around Graph Databases And Why It Matters". Forbes. Retrieved 2020-12-09.
  12. ^ ltd, Research and Markets. "Graph Database Market by Type (RDF and Property Graph), Application (Recommendation Engines, Fraud Detection, Risk and Compliance Management), Component (Tools and Services), Deployment Mode, Industry Vertical, and Region - Global Forecast to 2024". researchandmarkets.com. Retrieved 2020-12-09.
  13. ^ "GraphDB System Properties". db-engines.com. Retrieved 2021-04-15.
  14. ^ "GraphDB". Capterra. Retrieved 2020-12-09.
  15. ^ Ledvinka, Martin (2015). "JOPA: Accessing Ontologies in an Object-oriented Way" (PDF). Retrieved 2021-04-15.
  16. ^ Kiryakov, Atanas (November 2005). "OWLIM—a pragmatic semantic repository for OWL". ResearchGate. Retrieved 2021-04-15.
  17. ^ Stoilos, Giorgos; Grau, Bernardo Cuenca; Horrocks, Ian (2010-07-05). "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.
  18. ^ 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.
  19. ^ "SQL Access over JDBC". GraphDB documentation. Ontotext. Retrieved 5 October 2022.
  20. ^ "Kafka Sink Connector¶". GraphDB documentation. Ontotext. Retrieved 5 October 2022.
  21. ^ "Semantic Objects: Overview". Ontotext Platform documentation. Ontotext. Retrieved 5 October 2022.
  22. ^ "Semantic Search: Overview". Ontotext Platform documentation. Ontotext. Retrieved 5 October 2022.
  23. ^ "GraphDB Reasoning: predefined rulesets". GraphDB documentation. Ontotext. Retrieved 5 October 2022.
  24. ^ "Ontotext Refine: Overview and features". Ontotext Platform documentation. Ontotext. Retrieved 5 October 2022.
  25. ^ "Semantic similarity searches". GraphDB documentation. Ontotext. Retrieved 5 October 2022.
  26. ^ 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.
  27. ^ 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.
  28. ^ Zhuhadar, Leyla; Ciampa, Mark (2019-03-01). "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.
  29. ^ Damiano, Rossana; Lombardo, Vincenzo; Lieto, Antonio; Borra, Davide (2016-07-01). "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.
  30. ^ Panasiuk, Oleksandra (2019). "Representing GeoData for Tourism with Schema.org" (PDF). Retrieved 2021-04-15.
  31. ^ 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 2021-04-15
  32. ^ Nundloll, Vatsala; Lamb, Rob; Hankin, Barry; Blair, Gordon (2021-04-01). "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.
  33. ^ Quaresma, Paulo (2020). "Information Extraction from Historical Texts:a Case Study" (PDF). Retrieved 2021-04-15.
  34. ^ 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.
  35. ^ "BBC - BBC Internet Blog: Sports Refresh: Dynamic Semantic Publishing". BBC. Retrieved 2020-12-09.
  36. ^ "BBC - BBC Internet Blog: BBC World Cup 2010 dynamic semantic publishing". BBC. Retrieved 2020-12-09.
  37. ^ "Semantic Technology for online, broadcast and print media". videolectures.net. Retrieved 2020-12-09.
  38. ^ "SciGraph | For Researchers". Springer Nature. Retrieved 2020-12-09.
  39. ^ "Linked Government Data". nationalarchives.gov.uk. Retrieved 2020-12-09.
  40. ^ "Performance testing a graph database | Parliamentary Digital Service". pds.blog.parliament.uk. Retrieved 2021-04-15.
  41. ^ Anadiotis, George. "Graph databases and RDF: It's a family affair". ZDNet. Retrieved 2020-12-09.
  42. ^ Bishop, Barry (January 2011). "OWLIM: A family of scalable semantic repositories". ResearchGate. Retrieved 2021-04-15.
  43. ^ Alexiev, Vladimir (March 2021). "Diverse Uses of a Semantic Graph Database for Knowledge Organization and Research" (PDF). European Data Conference on Reference Data and Semantics (ENDORSE 2021).
  44. ^ Alexiev, Vladimir. "Diverse Uses of Ontotext GraphDB". YouTube video.
  45. ^ Alexiev, Vladimir. "Diverse Uses of Ontotext GraphDB". GitHub project.
  46. ^ "Ontotext-GraphDB". Zotero shared bibliography. Retrieved 5 October 2022.
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