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2025 | OriginalPaper | Chapter

Compiling SHACL Into SQL

Authors : Maxime Jakubowski, Jan Van den Bussche

Published in: The Semantic Web – ISWC 2024

Publisher: Springer Nature Switzerland

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Abstract

Constraints on graph data expressed in the Shapes Constraint Language (SHACL) can be quite complex. This brings the challenge of efficient validation of complex SHACL constraints on graph data. This challenge is remarkably similar to the processing of analytical queries, investigated intensively in the database community. Motivated by this observation, we have devised an efficient compilation technique from SHACL into SQL, under a natural relational representation of RDF graphs. Our conclusion is that the powerful processing and optimization techniques, already offered by modern SQL engines, are more than up to the challenge.

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previous chapter Expanding the Scope: Inductive Knowledge Graph Reasoning with Multi-starting Progressive Propagation
next chapter DUNKS: Chunking and Summarizing Large and Heterogeneous Data for Dataset Search
Footnotes
1
An alternative, automata-based, but equally interesting and popular approach to shapes is taken in the language ShEx [14, 29, 55].
 
2
In the early days of data analytics, analytical querying was referred to as OLAP and was typically focused on data cube operations [31, 43]. Later, the term broadened to complex SQL querying; it is in this sense that we use the term “analytical query” in the present paper.
 
3
We are leaving as out of scope a discussion of the advances in cloud database systems.
 
4
Alternatively we could have used the Hyper API https://​www.​tableau.​com/​developer/​tools/​hyper-api.
 
5
This operator is defined by the Operator Mapping in SPARQL [32].
 
6
Actually, in the case of targetclass, we simplify to only targeting direct type declarations.
 
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Metadata
Title
Compiling SHACL Into SQL
Authors
Maxime Jakubowski
Jan Van den Bussche
Copyright Year
2025
Book
The Semantic Web – ISWC 2024

Print ISBN: 978-3-031-77849-0

Electronic ISBN: 978-3-031-77850-6

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-77850-6_4

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