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Incorporating Functions in Mappings to Facilitate the Uplift of CSV Files into RDF

  • Ademar Crotti JuniorEmail author
  • Christophe Debruyne
  • Declan O’Sullivan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9989)

Abstract

Many solutions have been developed to convert non-RDF data to RDF. A common task during this conversion is applying data manipulation functions to obtain the desired output. Depending on the data format of the source to be transformed, one can rely on the underlying technology, such as RDBMS for relational databases or XQuery for XML, to manipulate data - to a certain extent - while generating RDF. For CSV files, however, there is no such underlying technology. Instead, one has to resort to more elaborate Extract, Transform and Load (ETL) processes, which can render the generation of RDF more complex (in terms of number of steps), and therefore also less traceable and transparent. One solution to this problem is the declaration and inclusion of functions in mappings of non-RDF data to RDF. In this paper, we propose a method to incorporate functions into mapping languages and demonstrate its viability in Digital Humanities use case.

Keywords

Linked Data Mapping Data manipulation 

Notes

Acknowledgements

This study is supported by: (i) CNPQ, National Counsel of Technological and Scientific Development – Brazil; (ii) the Science Foundation Ireland ADAPT Centre for Digital Content Technology (Grant 13/RC/2106); (iii) John Templeton Foundation grant to the Evolution Institute [https://evolution-institute.org/project/seshat/]; (iv) the European Union Horizon 2020 ALIGNED [www.aligned-project.eu] (Grant 644055).

References

  1. 1.
    Dimou, A., Vander Sande, M., Colpaert, P., Verborgh, R., Mannens, E., Van de Walle, R.: RML: a generic language for integrated RDF mappings of heterogeneous data. In: Workshop on Linked Data on the Web (2014)Google Scholar
  2. 2.
    Dimou, A., De Nies, T., Verborgh, R., Mannens, E., and Van de Walle, R.: Automated metadata generation for Linked Data generation and publishing workflows. In: Workshop on Linked Data on the Web (2016)Google Scholar
  3. 3.
    Stadler, C., Unbehauen, J., Westphal, P., Sherif, M.A., Lehmann, J.: Simplified RDB2RDF mapping. In: Workshop on Linked Data on the Web (2015)Google Scholar
  4. 4.
    Debruyne, C., O’Sullivan, D.: R2RML-F: towards sharing and executing domain logic in R2RML mappings. In: Workshop on Linked Data on the Web (2016)Google Scholar
  5. 5.
    Bischof, S., Decker, S., Krennwallner, T., Lopes, N., Polleres, A.: Mapping between RDF and XML with XSPARQL. J. Data Semant. 1(3), 147–185 (2012)CrossRefGoogle Scholar
  6. 6.
    Turchin, P., Brennan, R., Currie, T., Feeney, K., Francois, P., Hoyer, D., Manning, J., Marciniak, A., Mullins, D., Palmisano, A., et al.: Seshat: the global history data-bank. Cliodynamics J. Quant. Hist. Cult. Evol. 6, 77–107 (2015)Google Scholar
  7. 7.
    Slepicka, J., Yin, C., Szekely, P., Knoblock, C.: KR2RML: an alternative interpretation of R2RML for heterogeneous sources. In: Proceedings of the 6th International Workshop on Consuming Linked Data (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ademar Crotti Junior
    • 1
    Email author
  • Christophe Debruyne
    • 1
  • Declan O’Sullivan
    • 1
  1. 1.ADAPT Centre for Digital Content Technology Research, Knowledge and Data Engineering Group, School of Computer Science and StatisticsTrinity College DublinDublin 2Ireland

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