Sitt Min Oo

dblp:307/2313 · DBLP profile ↗
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3ranked-venue papers in the field
2as first author
3since 2021 · last 2025
0000-0001-9157-7507ORCID · corroborated

Domains — venue-derived; a paper can count in several

Knowledge Engineering, Semantic Web & Information Systems · 3 (2 first)
YearPublicationVenuePosition
2025 An Algebraic Foundation for Knowledge Graph Construction
Sitt Min Oo, Olaf Hartig
ESWC (1)1
2022 RMLStreamer-SISO: An RDF Stream Generator from Streaming Heterogeneous Data
Sitt Min Oo, Gerald Haesendonck, Ben De Meester, Anastasia Dimou
ISWC1
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-CAP3