Automatic extraction of shapes using sheXer

There is an increasing number of projects based on Knowledge Graphs and SPARQL endpoints. These SPARQL endpoints are later queried by final users or used to feed many different kinds of applications. Shape languages, such as ShEx and SHACL, have emerged to guide the evolution of these graphs and to validate their expected topology. However, authoring shapes for an existing knowledge graph is a time-consuming task. The task gets more challenging when dealing with sources, possibly maintained by heterogeneous agents. In this paper, we present sheXer, a system that extracts shapes by mining the graph structure. We offer sheXer as a free Python library capable of producing both ShEx and SHACL content. Compared to other automatic shape extractors, sheXer includes some novel features such as shape inter-linkage and computation of big real-world datasets. We analyze the features and limitations w.r.t. performance with different experiments using the English chapter of DBpedia.& nbsp;(c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces sheXer, a system that extracts shapes by mining the graph structure. sheXer is offered as a free Python library capable of producing both ShEx and SHACL content. Compared to other automatic shape extractors, sheXer includes some novel features such as shape inter-linkage and computation of big real-world datasets. The features and limitations of sheXer are analyzed with different experiments using the English chapter of DBpedia.