Thomas Delva

dblp:292/2101 · DBLP profile ↗
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4ranked-venue papers
2as first author
4since 2021 · last 2023
0000-0001-9521-2185ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 4 · 2 first-author · 4 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021
YearPublicationVenuePosition
2023 Data Provenance for SHACL
Thomas Delva, Anastasia Dimou, Maxime Jakubowski, Jan Van den Bussche
EDBT1
2023 Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review
Dylan Van Assche, Thomas Delva, Gerald Haesendonck, Pieter Heyvaert, Ben De Meester, Anastasia Dimou
J. Web Semant.2
2021 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
ICWE4
2021 RML2SHACL: RDF Generation Taking Shape
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.
Thomas Delva, Birte De Smedt, Sitt Min Oo, Dylan Van Assche, Sven Lieber, Anastasia Dimou
K-CAP1