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Turning Transport Data to Comply with EU Standards While Enabling a Multimodal Transport Knowledge Graph

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The Semantic Web – ISWC 2020 (ISWC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12507))

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Abstract

Complying with the EU Regulation on multimodal transportation services requires sharing data on the National Access Points in one of the standards (e.g., NeTEx and SIRI) indicated by the European Commission. These standards are complex and of limited practical adoption. This means that datasets are natively expressed in other formats and require a data translation process for full compliance.

This paper describes the solution to turn the authoritative data of three different transport stakeholders from Italy and Spain into a format compliant with EU standards by means of Semantic Web technologies. Our solution addresses the challenge and also contributes to build a multi-modal transport Knowledge Graph of interlinked and interoperable information that enables intelligent querying and exploration, as well as facilitates the design of added-value services.

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Notes

  1. 1.

    EU Reg. 2017/1926, cf. https://eur-lex.europa.eu/eli/reg_del/2017/1926/oj.

  2. 2.

    NeTEx, cf. http://netex-cen.eu/.

  3. 3.

    SIRI, cf. http://www.transmodel-cen.eu/standards/siri/.

  4. 4.

    SNAP (Seamless exchange of multi-modal transport data for transition to National Access Points), cf. https://www.snap-project.eu.

  5. 5.

    Transmodel, cf. http://www.transmodel-cen.eu/.

  6. 6.

    Talend4SW, cf. https://github.com/fbelleau/talend4sw.

  7. 7.

    RDFBeans, cf. https://github.com/cyberborean/rdfbeans.

  8. 8.

    Empire, cf. https://github.com/mhgrove/Empire.

  9. 9.

    Apache Camel, cf. https://camel.apache.org/.

  10. 10.

    Spring, cf. https://spring.io/.

  11. 11.

    RDF4J, cf. https://rdf4j.org/.

  12. 12.

    Cf. https://github.com/cefriel/rmlmapper-cefriel.

  13. 13.

    Cf. https://github.com/RMLio/rmlmapper-java.

  14. 14.

    We borrowed the idea from the Carml implementation of RML, cf. https://github.com/carml/carml#input-stream-extension.

  15. 15.

    Cf. https://velocity.apache.org/.

  16. 16.

    We implemented this approach both as a Chimera block and as a standalone tool available at https://github.com/cefriel/rdf-lowerer.

  17. 17.

    Cf. for instance RDFS inference on in-memory/native RDF4J stores https://rdf4j.org/documentation/programming/repository/#rdf-schema-inferencing.

  18. 18.

    Cf. https://github.com/cefriel/chimera/tree/master/chimera-example.

  19. 19.

    CRTM, cf. https://www.crtm.es/.

  20. 20.

    EMT Madrid, cf. https://www.emtmadrid.es/.

  21. 21.

    SEA Aeroporti, cf. http://www.seamilano.eu/.

  22. 22.

    AMT Genova, cf. https://www.amt.genova.it/amt/.

  23. 23.

    Cf. https://w3id.org/transmodel/.

  24. 24.

    Fiware, cf. https://www.fiware.org/.

  25. 25.

    GraphDB, cf. http://graphdb.ontotext.com/documentation/9.0/free/.

  26. 26.

    Cf. http://sprint-transport.eu/.

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Acknowledgments

The presented research was partially supported by the SPRINT project (Grant Agreement 826172), co-funded by the European Commission under the Horizon 2020 Framework Programme and by the SNAP project (Activity Id 19281) co-funded by EIT Digital in the Digital Cities Action Line.

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Authors and Affiliations

  1. Cefriel – Politecnico of Milano, Viale Sarca 226, 20126, Milan, Italy

    Mario Scrocca, Marco Comerio, Alessio Carenini & Irene Celino

Authors
  1. Mario Scrocca
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  2. Marco Comerio
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  3. Alessio Carenini
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  4. Irene Celino
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Corresponding author

Correspondence to Mario Scrocca .

Editor information

Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Jeff Z. Pan

  2. University of Liverpool, Liverpool, UK

    Valentina Tamma

  3. University of Bari, Bari, Italy

    Claudia d’Amato

  4. University of California, Santa Barbara, Santa Barbara, CA, USA

    Krzysztof Janowicz

  5. California State University, Long Beach, Long Beach, CA, USA

    Bo Fu

  6. Vienna University of Economics and Business, Vienna, Austria

    Axel Polleres

  7. Rensselaer Polytechnic Institute, Troy, NY, USA

    Oshani Seneviratne

  8. Massachusetts Institute of Technology, Cambridge, MA, USA

    Lalana Kagal

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Scrocca, M., Comerio, M., Carenini, A., Celino, I. (2020). Turning Transport Data to Comply with EU Standards While Enabling a Multimodal Transport Knowledge Graph. In: Pan, J.Z., et al. The Semantic Web – ISWC 2020. ISWC 2020. Lecture Notes in Computer Science(), vol 12507. Springer, Cham. https://doi.org/10.1007/978-3-030-62466-8_26

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  • DOI: https://doi.org/10.1007/978-3-030-62466-8_26

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