Leveraging Web of Things W3C recommendations for knowledge graphs generation
- Author
- Dylan Van Assche (UGent) , Gerald Haesendonck (UGent) , Gertjan De Mulder (UGent) , Thomas Delva (UGent) , Pieter Heyvaert (UGent) , Ben De Meester (UGent) and Anastasia Dimou (UGent)
- Organization
- 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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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8708752
- MLA
- Van Assche, Dylan, et al. “Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation.” WEB ENGINEERING, ICWE 2021, edited by Marco Brambilla et al., vol. 12706, Springer, 2021, pp. 337–52, doi:10.1007/978-3-030-74296-6_26.
- APA
- Van Assche, D., Haesendonck, G., De Mulder, G., Delva, T., Heyvaert, P., De Meester, B., & Dimou, A. (2021). Leveraging Web of Things W3C recommendations for knowledge graphs generation. In M. Brambilla, R. Chbeir, F. Frasincar, & I. Manolescu (Eds.), WEB ENGINEERING, ICWE 2021 (Vol. 12706, pp. 337–352). https://doi.org/10.1007/978-3-030-74296-6_26
- Chicago author-date
- Van Assche, Dylan, Gerald Haesendonck, Gertjan De Mulder, Thomas Delva, Pieter Heyvaert, Ben De Meester, and Anastasia Dimou. 2021. “Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation.” In WEB ENGINEERING, ICWE 2021, edited by Marco Brambilla, Richard Chbeir, Flavius Frasincar, and Ioana Manolescu, 12706:337–52. Springer. https://doi.org/10.1007/978-3-030-74296-6_26.
- Chicago author-date (all authors)
- Van Assche, Dylan, Gerald Haesendonck, Gertjan De Mulder, Thomas Delva, Pieter Heyvaert, Ben De Meester, and Anastasia Dimou. 2021. “Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation.” In WEB ENGINEERING, ICWE 2021, ed by. Marco Brambilla, Richard Chbeir, Flavius Frasincar, and Ioana Manolescu, 12706:337–352. Springer. doi:10.1007/978-3-030-74296-6_26.
- Vancouver
- 1.Van Assche D, Haesendonck G, De Mulder G, Delva T, Heyvaert P, De Meester B, et al. Leveraging Web of Things W3C recommendations for knowledge graphs generation. In: Brambilla M, Chbeir R, Frasincar F, Manolescu I, editors. WEB ENGINEERING, ICWE 2021. Springer; 2021. p. 337–52.
- IEEE
- [1]D. Van Assche et al., “Leveraging Web of Things W3C recommendations for knowledge graphs generation,” in WEB ENGINEERING, ICWE 2021, Biarritz, France, 2021, vol. 12706, pp. 337–352.
@inproceedings{8708752,
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.}},
author = {{Van Assche, Dylan and Haesendonck, Gerald and De Mulder, Gertjan and Delva, Thomas and Heyvaert, Pieter and De Meester, Ben and Dimou, Anastasia}},
booktitle = {{WEB ENGINEERING, ICWE 2021}},
editor = {{Brambilla, Marco and Chbeir, Richard and Frasincar, Flavius and Manolescu, Ioana}},
isbn = {{9783030742959}},
issn = {{0302-9743}},
language = {{eng}},
location = {{Biarritz, France}},
pages = {{337--352}},
publisher = {{Springer}},
title = {{Leveraging Web of Things W3C recommendations for knowledge graphs generation}},
url = {{http://dx.doi.org/10.1007/978-3-030-74296-6_26}},
volume = {{12706}},
year = {{2021}},
}
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