Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation

Dylan Van Assche, Gerald Haesendonck, Gertjan De Mulder, Thomas Delva, Pieter Heyvaert , Ben De Meester , Anastasia Dimou
In Web Engineering, 21st International Conference, ICWE 2021, Biarritz, France, May 18–21, 2021 (2021)

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., databasesor files. However, such mapping languages have limited support for de-scribing 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 releaseda 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 suchdescriptions can be used to indicate how the generated knowledge graph should be exported. We extended RML's Logical Source to support WoTdescriptions of Web APIs and streams, and introduced RML's LogicalTarget 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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BibTeX +

@InProceedings{VanAssche2021LeveragingWebThings,
    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, 21st International Conference, ICWE 2021, Biarritz, France, May 18--21, 2021},
    title     = {{Leveraging Web of Things W3C Recommendations for Knowledge Graphs Generation}},
    year      = {2021},
    address   = {Cham},
    editor    = {Brambilla, Marco and Chbeir, Richard and Frasincar, Flavius and Manolescu, Ioana},
    month     = may,
    number    = {1},
    pages     = {337--352},
    publisher = {Springer},
    series    = {Lecture Notes in Computer Science},
    volume    = {12706},
    doi       = {10.1007/978-3-030-74296-6_26},
    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., databasesor files. However, such mapping languages have limited support for de-scribing 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 releaseda 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 suchdescriptions can be used to indicate how the generated knowledge graph should be exported. We extended RML's Logical Source to support WoTdescriptions of Web APIs and streams, and introduced RML's LogicalTarget 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.},
    pdf       = {https://biblio.ugent.be/publication/8708752/file/8708753},

}