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

SoftwareX 2026 Open access

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Authors: Daniel Fernández-Álvarez, Jose Emilio Labra-Gayo
DOI: 10.1016/j.softx.2026.102592
Cited by: 1
References: 10

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.

Fields of Study & Subjects

Semantic Web and Ontologies Digital Humanities and Scholarship Scientific Computing and Data Management

Publication details

Journal / source
SoftwareX
Publisher
Elsevier BV
Volume
34
Pages
102592–102592
Publication date
12 March 2026
Language
EN