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International Conference on Web Engineering

ICWE 2021: Web Engineering pp 337–352Cite as

Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12706))

Abstract

Constructing a knowledge graph with mapping languages, such as RML or SPARQL-Generate, allows seamlessly integrating heterogeneous data by defining access-specific definitions for e.g., databases or files. However, such mapping languages have limited support for describing Web APIs and no support for describing data with varying velocities, as needed for e.g., streams, neither for the input data nor for the output RDF. This hampers the smooth and reproducible generation of knowledge graphs from heterogeneous data and their continuous integration for consumption since each implementation provides its own extensions. Recently, the Web of Things (WoT) Working Group released a set of recommendations to provide a machine-readable description of metadata and network-facing interfaces for Web APIs and streams. In this paper, we investigated (i) how mapping languages can be aligned with the newly specified recommendations to describe and handle heterogeneous data with varying velocities and Web APIs, and (ii) how such descriptions can be used to indicate how the generated knowledge graph should be exported. We extended RML’s Logical Source to support WoT descriptions of Web APIs and streams, and introduced RML’s Logical Target to describe the generated knowledge graph reusing the same descriptions. We implemented these extensions in the RMLMapper and RMLStreamer, and validated our approach in two use cases. Mapping languages are now able to use the same descriptions to define the input data but also the output RDF. This way, our work paves the way towards more reproducible workflows for knowledge graph generation.

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Change history

  • 11 May 2021

    In Chapter 10, the term “paths” was used instead of the term “operation.” This has been corrected and the term “operations” is now used throughout the paper.

Notes

  1. 1.

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

  2. 2.

    https://github.com/RMLio/rmlstreamer.

  3. 3.

    https://github.com/carml/carml.

  4. 4.

    https://www.imec-int.com/en/what-we-offer/research-portfolio/essence.

  5. 5.

    https://open-livedata.antwerpen.be/#/org/digipolis/api/weerobservatiecutler-actuelewaarden/v1/documentation.

  6. 6.

    https://telraam-api.net/.

  7. 7.

    https://www.imec-int.com/en/what-we-offer/research-portfolio/daiquiri.

  8. 8.

    https://github.com/kg-construct/mapping-challenges/issues.

  9. 9.

    CARML’s Stream: https://github.com/carml/carml

    RMLStreamer’s RML extension: https://github.com/RMLio/rmlstreamer

    Chimera’s InputStream: https://github.com/cefriel/chimera.

  10. 10.

    https://github.com/kg-construct/mapping-challenges/issues/7.

  11. 11.

    https://w3id.org/idsa/core.

  12. 12.

    https://rml.io/specs/rml-target.

  13. 13.

    http://semweb.mmlab.be/ns/rml-target#.

  14. 14.

    http://semweb.mmlab.be/ns/rml-target#.

  15. 15.

    https://www.w3.org/ns/formats/.

  16. 16.

    http://semweb.mmlab.be/ns/rml-target#.

  17. 17.

    http://semweb.mmlab.be/ns/rml-compression#.

  18. 18.

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

  19. 19.

    https://github.com/RMLio/rmlstreamer.

  20. 20.

    https://github.com/RMLio/web-of-things-icwe2021.

  21. 21.

    https://github.com/RMLio/web-of-things-icwe2021.

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Authors and Affiliations

  1. IDLab, Department of Electronics and Information Systems, Ghent University – imec, Technologiepark-Zwijnaarde 122, 9052, Ghent, Belgium

    Dylan Van Assche, Gerald Haesendonck, Gertjan De Mulder, Thomas Delva, Pieter Heyvaert, Ben De Meester & Anastasia Dimou

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  1. Dylan Van Assche
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  2. Gerald Haesendonck
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  3. Gertjan De Mulder
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  4. Thomas Delva
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  5. Pieter Heyvaert
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  6. Ben De Meester
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  7. Anastasia Dimou
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Correspondence to Dylan Van Assche or Anastasia Dimou .

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Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy

    Marco Brambilla

  2. E2S UPPA, LIUPPA, Université de Pau et des Pays de l’Adour, Anglet, France

    Richard Chbeir

  3. Econometric Institute, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Flavius Frasincar

  4. Inria Saclay-Île-de-France, Institut Polytechnique de Paris, Palaiseau, France

    Ioana Manolescu

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Van Assche, D. et al. (2021). Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation. In: Brambilla, M., Chbeir, R., Frasincar, F., Manolescu, I. (eds) Web Engineering. ICWE 2021. Lecture Notes in Computer Science(), vol 12706. Springer, Cham. https://doi.org/10.1007/978-3-030-74296-6_26

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  • DOI: https://doi.org/10.1007/978-3-030-74296-6_26

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