Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/31220
DC FieldValueLanguage
dc.contributor.authorMarija Vecovskaen_US
dc.contributor.authorMilos Jovanoviken_US
dc.date.accessioned2024-08-30T19:07:39Z-
dc.date.available2024-08-30T19:07:39Z-
dc.date.issued2024-07-25-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/31220-
dc.description.abstractThis paper introduces RDFGraphGen, a general-purpose, domain-independent generator of synthetic RDF graphs based on SHACL constraints. The Shapes Constraint Language (SHACL) is a W3C standard which specifies ways to validate data in RDF graphs, by defining constraining shapes. However, even though the main purpose of SHACL is validation of existing RDF data, in order to solve the problem with the lack of available RDF datasets in multiple RDF-based application development processes, we envisioned and implemented a reverse role for SHACL: we use SHACL shape definitions as a starting point to generate synthetic data for an RDF graph. The generation process involves extracting the constraints from the SHACL shapes, converting the specified constraints into rules, and then generating artificial data for a predefined number of RDF entities, based on these rules. The purpose of RDFGraphGen is the generation of small, medium or large RDF knowledge graphs for the purpose of benchmarking, testing, quality control, training and other similar purposes for applications from the RDF, Linked Data and Semantic Web domain. RDFGraphGen is open-source and is available as a ready-to-use Python package.en_US
dc.language.isoenen_US
dc.subjectData Generatoren_US
dc.subjectSynthetic Dataen_US
dc.subjectKnowledge Graphsen_US
dc.subjectRDFen_US
dc.subjectSHACLen_US
dc.subjectSemantic Weben_US
dc.titleRDFGraphGen: A Synthetic RDF Graph Generator based on SHACL Constraintsen_US
dc.typePreprinten_US
dc.identifier.doi10.48550/arXiv.2407.17941-
item.grantfulltextopen-
item.fulltextWith Fulltext-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
Files in This Item:
File Description SizeFormat 
2407.17941v1.pdf179.78 kBAdobe PDFView/Open
Show simple item record

Page view(s)

69
checked on May 3, 2025

Download(s)

4
checked on May 3, 2025

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.