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RML2SHACL : RDF generation is shaping up

Thomas Delva, Birte De Smedt, Sitt Min Oo (UGent) , Dylan Van Assche (UGent) , Sven Lieber and Anastasia Dimou (UGent)
(2021) PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP '21). p.153-160
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Abstract
RDF graphs are often generated by mapping data in other (semi-)structured data formats to RDF. Such mapped graphs have a repetitive structure defined by (i) the mapping rules and (ii) the schema of the input sources. However, this information is not exploited beyond its original scope. SHACL was recently introduced to model constraints that RDF graphs should validate. SHACL shapes and their constraints are either manually defined or derived from ontologies or RDF graphs. We investigate a method to derive the shapes and their constraints from mapping rules, allowing the generation of the RDF graph and the corresponding shapes in one step. In this paper, we present RML2SHACL: an approach to generate SHACL shapes that validate RDF graphs defined by RML mapping rules. RML2SHACL relies on our proposed set of correspondences between RML and SHACL constructs. RML2SHACL covers a large variety of RML constructs, as proven by generating shapes for the RML test cases. A comparative analysis shows that shapes generated by RML2SHACL are similar to shapes generated by ontology-based tools, with a larger focus on data value-based constraints instead of schema-based constraints. We also found that RML2SHACL has a faster execution time than data-graph based approaches for data sizes of 90MB and higher.
Keywords
Shape generation, RDF shapes, SHACL, R2RML, RML

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MLA
Delva, Thomas, et al. “RML2SHACL : RDF Generation Is Shaping Up.” PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP ’21), Association for Computing Machinery (ACM), 2021, pp. 153–60, doi:10.1145/3460210.3493562.
APA
Delva, T., De Smedt, B., Min Oo, S., Van Assche, D., Lieber, S., & Dimou, A. (2021). RML2SHACL : RDF generation is shaping up. PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP ’21), 153–160. https://doi.org/10.1145/3460210.3493562
Chicago author-date
Delva, Thomas, Birte De Smedt, Sitt Min Oo, Dylan Van Assche, Sven Lieber, and Anastasia Dimou. 2021. “RML2SHACL : RDF Generation Is Shaping Up.” In PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP ’21), 153–60. Association for Computing Machinery (ACM). https://doi.org/10.1145/3460210.3493562.
Chicago author-date (all authors)
Delva, Thomas, Birte De Smedt, Sitt Min Oo, Dylan Van Assche, Sven Lieber, and Anastasia Dimou. 2021. “RML2SHACL : RDF Generation Is Shaping Up.” In PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP ’21), 153–160. Association for Computing Machinery (ACM). doi:10.1145/3460210.3493562.
Vancouver
1.
Delva T, De Smedt B, Min Oo S, Van Assche D, Lieber S, Dimou A. RML2SHACL : RDF generation is shaping up. In: PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP ’21). Association for Computing Machinery (ACM); 2021. p. 153–60.
IEEE
[1]
T. Delva, B. De Smedt, S. Min Oo, D. Van Assche, S. Lieber, and A. Dimou, “RML2SHACL : RDF generation is shaping up,” in PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP ’21), Online, 2021, pp. 153–160.
@inproceedings{8734910,
  abstract     = {{RDF graphs are often generated by mapping data in other (semi-)structured data formats to RDF. Such mapped graphs have a repetitive structure defined by (i) the mapping rules and (ii) the schema of the input sources. However, this information is not exploited beyond its original scope. SHACL was recently introduced to model constraints that RDF graphs should validate. SHACL shapes and their constraints are either manually defined or derived from ontologies or RDF graphs. We investigate a method to derive the shapes and their constraints from mapping rules, allowing the generation of the RDF graph and the corresponding shapes in one step. In this paper, we present RML2SHACL: an approach to generate SHACL shapes that validate RDF graphs defined by RML mapping rules. RML2SHACL relies on our proposed set of correspondences between RML and SHACL constructs. RML2SHACL covers a large variety of RML constructs, as proven by generating shapes for the RML test cases. A comparative analysis shows that shapes generated by RML2SHACL are similar to shapes generated by ontology-based tools, with a larger focus on data value-based constraints instead of schema-based constraints. We also found that RML2SHACL has a faster execution time than data-graph based approaches for data sizes of 90MB and higher.}},
  author       = {{Delva, Thomas and De Smedt, Birte and Min Oo, Sitt and Van Assche, Dylan and Lieber, Sven and Dimou, Anastasia}},
  booktitle    = {{PROCEEDINGS OF THE 11TH KNOWLEDGE CAPTURE CONFERENCE (K-CAP '21)}},
  isbn         = {{9781450384575}},
  keywords     = {{Shape generation,RDF shapes,SHACL,R2RML,RML}},
  language     = {{eng}},
  location     = {{Online}},
  pages        = {{153--160}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{RML2SHACL : RDF generation is shaping up}},
  url          = {{http://doi.org/10.1145/3460210.3493562}},
  year         = {{2021}},
}
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