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.

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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
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article

sheXer: A robust and versatile library for automatic extraction of RDF shapes

Daniel Fernández-Álvarez, Jose Emilio Labra-Gayo
SoftwareX
Semantic Web and Ontologies
article

sheXer: A robust and versatile library for automatic extraction of RDF shapes

Daniel Fernández-Álvarez, Jose Emilio Labra-Gayo
article en

Abstract

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.

SoftwareXVol. 34
Universidad de Oviedo (ES)
Quality Education
Openalex Percentile: Top 28%
Semantic Web and Ontologies
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