sheXer: A robust and versatile library for automatic extraction of RDF shapes
RDF shapes are effective tools for describing and validating RDF content. However, producing and maintaining them is a time-consuming task that typically requires domain experts with technical skills. In this paper, we present sheXer, an open-source Python library for the automatic extraction of RDF shapes from existing RDF instance data. sheXer supports a wide range of use cases by accepting multiple input formats (e.g., text files, SPARQL endpoints, compressed content), producing different types of outputs (SHACL, ShEx, UML visualizations, RDF-Config files, statistics), and enabling extractions tailored to specific goals.
Authors
- Daniel Fernández-Álvarez (ORCID: https://orcid.org/0000-0002-8666-7660)
- Jose Emilio Labra-Gayo
Institutions
- Universidad de Oviedo (ES)
Publication Details
- Journal
- SoftwareX
- Published
- 2026-03-12
- DOI
- https://doi.org/10.1016/j.softx.2026.102592
- Primary Topic
- Semantic Web and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00