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Connecting Knowledge to Data Through Transformations in KnowID: System Description

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Abstract

Intelligent information systems deploy applied ontologies or logic-based conceptual data models for effective and efficient data management and to assist with decision-making. A core deliberation in the design of such systems, is how to link the knowledge to the data. We recently designed a novel knowledge-to-data architecture (KnowID) which aims to solve this critical step through a set of transformation rules rather than a mapping layer, which operate between models represented in EER notation and an enhanced relational model called the ARM. This system description zooms in on the novel tool for the core component of the transformation from the Artificial Intelligence-oriented modelling to the relational database-oriented data management. It provides an overview of the requirements, design, and implementation of the modular transformations module that straightforwardly permits extension with other components of the modular KnowID architecture.

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Notes

  1. The team members kindly permitted us to use and revise their code (by default they are the copyright holders).

References

  1. Borgida A, Toman D, Weddell GE (2016) On referring expressions in information systems derived from conceptual modelling. In: Proc. of ER’16, LNCS, vol. 9974, pp 183–197. Springer

  2. Braun G, Estevez E, Fillottrani P (2019) A reference architecture for ontology engineering web environments. J Comp Sci Tech 19(1):22–31

    Google Scholar 

  3. Calvanese D, Cogrel B, Komla-Ebri S, Kontchakov R, Lanti D, Rezk M, Rodriguez-Muro M, Xiao G (2017) Ontop: Answering SPARQL queries over relational databases. Sem Web J 8(3):471–487

    Article  Google Scholar 

  4. Calvanese D, De Giacomo G, Lembo D, Lenzerini M, Rosati R (2007) Eql-lite: effective first-order query processing in description logics. In: Veloso MM (ed) IJCAI 2007, Proceedings of the 20th International Joint Conference on artificial intelligence, Hyderabad, India, January 6–12, 2007, pp 274–279

  5. Davis I, Steiner T, Hors AJL (2013) RDF 1.1 JSON Alternate Serialization (RDF/JSON) (2013). http://www.w3.org/TR/rdf-json/. Accessed 26 June 2020

  6. Dimou A, Sande MV (2014) RDF mapping language (RML). Unofficial draft, Ghent University (2014).http://rml.io/spec.html. Accessed 26 June 2020

  7. Fillottrani PR, Keet CM (2014) Conceptual model interoperability: a metamodel-driven approach. In: Bikakis A, et al. (ed) Proc. of RuleML’14, LNCS, vol. 8620, pp 52–66. Springer

  8. Fillottrani PR, Keet CM (2015) Evidence-based languages for conceptual data modelling profiles. In: Morzy T, et al. (ed) Proc. of ADBIS’15, LNCS, vol. 9282, pp 215–229. Springer

  9. Fillottrani PR, Keet CM (2020) KnowID: an architecture for efficient knowledge-driven information and data access. Data Intell. https://doi.org/10.1162/dint_a_00060 (in print)

    Article  Google Scholar 

  10. Gottlob G, Kikot S, Kontchakov R, Podolskii VV, Schwentick T, Zakharyaschev M (2014) The price of query rewriting in ontology-based data access. Artif Intell 213:42–59

    Article  MathSciNet  Google Scholar 

  11. Kontchakov R, Lutz C, Toman D, Wolter F, Zakharyaschev M (2010) The combined approach to query answering in dl-lite. In: Lin F, Sattler U, Truszczynski M (eds) Principles of knowledge representation and reasoning: proceedings of the Twelfth International Conference, KR 2010, Toronto, Ontario, Canada, May 9–13, 2010. AAAI Press

  12. Krötzsch M, Rudolph S (2016) Is your database system a semantic web reasoner? Künstl Intell 30:169–176

    Article  Google Scholar 

  13. Lubyte L, Tessaris S (2009) Automated extraction of ontologies wrapping relational data sources. In: Proc of DEXA’09, pp 128–142. Springer

  14. Ma W, Keet CM, Oldford W, Toman D, Weddell G (2018) The utility of the abstract relational model and attribute paths in SQL. In: Faron Zucker C et al. (ed) Proc. of EKAW’18, pp 195–211. Springer

  15. Noy N, Gao Y, Jain A, Narayanan A, Patterson A, Taylor J (2019) Industry-scale knowledge graphs: lessons and challenges. Queue 20(48–20):75 17(2)

    Google Scholar 

  16. Roy S, Suciu D (2014) A formal approach to finding explanations for database queries. In: Dyreson CE, Li F, Özsu MT (eds) International Conference on management of data, SIGMOD 2014, Snowbird, UT, USA, June 22-27, 2014, pp. 1579–1590. ACM. https://doi.org/10.1145/2588555.2588578

  17. Thalheim B (2009) Extended entity relationship model. In: Liu L, Özsu MT (eds) Encyclopedia of database systems, vol 1. Springer, Berlin, pp 1083–1091

    Chapter  Google Scholar 

  18. Toman D, Weddell GE (2011) Fundamentals of Physical design and query compilation. Synthesis lectures on data management. Morgan & Claypool Publishers, San Rafael

    MATH  Google Scholar 

  19. Toman D, Weddell GE (2014) On adding inverse features to the description logic \({CFD^{\forall }}_{{\rm nc}}\). In: Proc. of PRICAI’14, pp 587–599

  20. Xiao G, Ding L, Cogrel B, Calvanese D (2019) Virtual knowledge graphs: an overview of systems and use cases. Data Intell 1:201–223

    Article  Google Scholar 

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Acknowledgements

We would like to thank all the capstone students for their efforts, and in particular J. Du Plessis, St J. Grimbly, and G. Stein for their design and code. SJ and CMK acknowledge the partial support by the NRF of South Africa (Grant number 115376).

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Authors and Affiliations

  1. Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur, Bahía Blanca, Argentina and Comisión de Investigaciones Científicas, Bahía Blanca, Argentina

    Pablo R. Fillottrani

  2. Comisión de Investigaciones Científicas, Buenos Aires, Provincia de Buenos Aires, Argentina

    Pablo R. Fillottrani

  3. Department of Computer Science, University of Cape Town, Cape Town, South Africa

    Stephan Jamieson & C. Maria Keet

Authors
  1. Pablo R. Fillottrani
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  2. Stephan Jamieson
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  3. C. Maria Keet
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Correspondence to C. Maria Keet.

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Fillottrani, P.R., Jamieson, S. & Keet, C.M. Connecting Knowledge to Data Through Transformations in KnowID: System Description. Künstl Intell 34, 373–379 (2020). https://doi.org/10.1007/s13218-020-00675-6

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  • DOI: https://doi.org/10.1007/s13218-020-00675-6

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