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RAMP Shapes: Declarative RDF \(\leftrightarrow \) ADT Mapping

  • Alexey MorozovEmail author
  • Gerhard Wohlgenannt
  • Dmitry Mouromtsev
  • Dmitry Pavlov
  • Yury Emelyanov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1057)

Abstract

With the broad availability of Linked Data efficient and convenient programmatic access to semantic graph data within programming languages is important for using the data in applications. To this end, we present RAMP (RDF Algebraic Data Type Mapping), a type construction language, specification and an implementation of mapping operations between RDF graphs and structured data types. RAMP is based on algebraic data types, and aims to overcome limitations of existing approaches for example regarding the set of supported language constructs, or the ability to generalize over a different programming languages. At the same time, RAMP focuses on providing computationally efficient mapping operations.

Keywords

RDF ADT Data mapping JSON-LD 

Notes

Acknowledgements

This work was supported by the Government of the Russian Federation (Grant 074-U01) through the ITMO Fellowship and Professorship Program. Furthermore, the work is supported by Sputniq GmbH.

References

  1. 1.
    Akhtar, W., Kopecký, J., Krennwallner, T., Polleres, A.: XSPARQL: traveling between the XML and RDF worlds – and avoiding the XSLT pilgrimage. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 432–447. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-68234-9_33CrossRefGoogle Scholar
  2. 2.
    Boag, S., et al.: XQuery 1.0: an XML query language (2002)Google Scholar
  3. 3.
    Cohen, A., Herrmann, C.: Towards a high-productivity and high-performance marshaling library for compound data. In: 2nd MetaOCaml Workshop (2005)Google Scholar
  4. 4.
    Corby, O., Faron-Zucker, C., Gandon, F.: A generic RDF transformation software and its application to an online translation service for common languages of linked data. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9367, pp. 150–165. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25010-6_9CrossRefGoogle Scholar
  5. 5.
    Johnson, S.C., et al.: YACC: Yet Another Compiler-Compiler, vol. 32. Bell Laboratories, Murray Hill (1975)Google Scholar
  6. 6.
    Knublauch, H., Kontokostas, D.: Shapes constraint language (SHACL). W3C Candidate Recommendation, vol. 11, no. 8 (2017)Google Scholar
  7. 7.
    Lisena, P., Troncy, R.: Transforming the JSON output of SPARQL queries for linked data clients. In: Companion of the The Web Conference 2018 on The Web Conference 2018, pp. 775–780. International WWW Conferences Steering Committee (2018)Google Scholar
  8. 8.
    Longley, D., Kellogg, G., Lanthaler, M., Sporny, M.: JSON-LD 1.0 processing algorithms and API, vol. 18. WWW Consortium (2015). www.w3.org/TR/json-ld-api/
  9. 9.
    Prud’hommeaux, E., Labra Gayo, J.E., Solbrig, H.: Shape expressions: an RDF validation and transformation language. In: Proceedings of the 10th International Conference on Semantic Systems, pp. 32–40. ACM (2014)Google Scholar
  10. 10.
    Snydman, S., Sanderson, R., Cramer, T.: The international image interoperability framework (IIIF): a community & technology approach for web-based images. In: Archiving Conference, pp. 16–21. no. 1. Society for Imaging Science and Technology (2015)Google Scholar
  11. 11.
    Sporny, M., Longley, D., Kellogg, G., Lanthaler, M., Lindström, N.: JSON-LD 1.0, vol. 16, p. 41. W3C Recommendation (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Software Engineering and Computer SystemsITMO UniversitySt. PetersburgRussia
  2. 2.Vismart Ltd.St. PetersburgRussia

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