Type Checking Program Code Using SHACL

Leinberger, Martin; Seifer, Philipp; Schon, Claudia; Lämmel, Ralf; Staab, Steffen

doi:10.1007/978-3-030-30793-6_23

Cited by 8 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…certain policies, such as GDPR requirements [35,2]. Other applications of shacl include type checking program code [27] and detecting metadata errors in clinical studies [22]. shacl is also used by the European Commission to facilitate data sharing, for example by validating metadata about public services against the recommended vocabularies [46].…”

Section: Adoption Of Shaclmentioning

confidence: 99%

A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs

Pareti¹,

Konstantinidis²

2021

Preprint

View full text Add to dashboard Cite

We present an introduction and a review of Shapes Constraint Language (shacl), the W3C recommendation language for validating rdf data. A shacl document describes a set of constraints on rdf nodes, and a graph is valid with respect to the document if its nodes satisfy these constraints. We revisit the basic concepts of the language, its constructs and components and their interaction. We review the different formal frameworks used to study this language and the different semantics proposed. We examine a number of related problems, from containment and satisfiability to the interaction of shacl with inference rules, and exhibit how different modellings of the language are useful for different problems. We also cover practical aspects of shacl, discussing its implementations and state of adoption, to present a holistic review useful to practitioners and theoreticians alike.

show abstract

Section: Adoption Of Shaclmentioning

confidence: 99%

A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs

Pareti¹,

Konstantinidis²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Normatively they impose schematic constraints on the evoluion of a graph, such that a triple store may automatically reject illegitimate configurations. Used informatively, they aid software developers in understanding graphs, or inform downstream applications, e.g., [21].…”

Section: Introductionmentioning

confidence: 99%

From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries

Seifer,

Hernández,

Lämmel

et al. 2024

Proceedings of the ACM Web Conference 2024

Self Cite

View full text Add to dashboard Cite

SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes allowing users to understand which data they can expect and which not. However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data: Shape constraints on the input graph may affect the output graph, but may no longer apply literally, and new shapes may be imposed by the query template. In this paper, we study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. We assume that the SPARQL CONSTRUCT query is fixed, e.g., being part of a program, whereas the input graphs adhere to input shape constraints but may otherwise vary over time and, thus, are mostly unknown. We study a fragment of SPARQL CONSTRUCT queries (SCCQ) and a fragment of SHACL (Simple SHACL). We formally define the problem of deriving the most restrictive set of Simple SHACL shapes that constrain the results from evaluating a SCCQ over any input graph restricted by a given set of Simple SHACL shapes. We propose and implement an algorithm that statically analyses input SHACL shapes and CONSTRUCT queries and prove its soundness and complexity.

show abstract

“…Knowledge of a schema helps data consumers to effectively formulate their SPARQL queries in the first place. Moreover, a shape schema may be specified at the data consumer's side, expressing the structural constraints required for the data to be usable by local applications (e.g., [38,44]).…”

Section: Introductionmentioning

confidence: 99%

Shape Fragments

Delva¹,

Dimou²,

Jakubowski³

et al. 2021

Preprint

View full text Add to dashboard Cite

In constraint languages for RDF graphs, such as ShEx and SHACL, constraints on nodes and their properties in RDF graphs are known as "shapes". Schemas in these languages list the various shapes that certain targeted nodes must satisfy for the graph to conform to the schema. Using SHACL, we propose in this paper a novel use of shapes, by which a set of shapes is used to extract a subgraph from an RDF graph, the so-called shape fragment. Our proposed mechanism fits in the framework of Linked Data Fragments. In this paper, (i) we define our extraction mechanism formally, building on recently proposed SHACL formalizations; (ii) we establish correctness properties, which relate shape fragments to notions of provenance for database queries; (iii) we compare shape fragments with SPARQL queries; (iv) we discuss implementation options; and (v) we present initial experiments demonstrating that shape fragments are a feasible new idea.

show abstract

Type Checking Program Code Using SHACL

Cited by 8 publications

References 16 publications

A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs

A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs

From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries

Shape Fragments

Contact Info

Product

Resources

About