SHACL Constraints with Inference Rules

Pareti, Paolo; Konstantinidis, George; Norman, Timothy J.; Şensoy, Murat

doi:10.1007/978-3-030-30793-6_31

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…Assuming graph G is valid w.r.t. to a shacl document M , the approach presented in [38], called schema expansion, allows us to predict whether the graph closure G ′ will still be valid w.r.t. M without having to validate G ′ against M .…”

Section: Inference Rules and The Schema Expansionmentioning

confidence: 99%

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

Pareti¹,

Konstantinidis²

2021

Preprint

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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.

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Section: Inference Rules and The Schema Expansionmentioning

confidence: 99%

“…M 1 will validate shape :ManagerS, but it might not validate :SubordinateS. Two approaches to compute the schema expansion are presented in [38], for datalog [9] inference rules without negation. The first based on the concept of critical instance [30], and the second an optimisation of the first.…”

Section: Inference Rules and The Schema Expansionmentioning

confidence: 99%

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

Pareti¹,

Konstantinidis²

2021

Preprint

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“…As explained in [25], SHACL rules can trigger ontological or non-ontological inferences. Ontological inferences derive facts that can be added to the model.…”

Section: Compliance Checking In Shaclmentioning

confidence: 99%

“…The validation facts, as well as new individuals, derived through SHACL are not mandatorily inserted in the ontology. The SHACL rules to model the reified I/O logic formulae in (3), (4), and (5) represent non-ontological inferences, in the sense explained in [25]: these rules are only functional to infer the truth value of is-lawful before the SHACL shape in ( 6) is validated.…”

Section: Serializing Reified I/o Logic In Shaclmentioning

confidence: 99%

Towards compliance checking in reified I/O logic via SHACL

Robaldo

2021

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law

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Reified Input/Output logic [29] has been recently proposed to handle natural language meaning in Input/Output logic [17]. So far, the research in reified I/O logic has focused only on KR issues, specifically on how to use the formalism for representing contextual meaning of norms (see [28]). This paper is the first attempt to investigate reasoning in reified I/O logic, specifically compliance checking. This paper investigates how to model reified I/O logic formulae in Shapes Constraint Language (SHACL) [2], a recent W3C recommendation for validating and reasoning with RDFs/OWL.

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“…For example, an ACA aggregating observations from methane detectors is not relevant in an environment where methane detectors are not present. To tackle this, we reuse an existing approach for checking rule applicability on triplestores in different scenarios [2]. We do this by extracting the notion of a triplestore schema for the particular scenario and devising an algorithm to check rule applicability against that schema.…”

Section: Description Of the Frameworkmentioning

confidence: 99%

A Policy Editor for Semantic Sensor Networks

Pareti,

Konstantinidis,

Norman

2019

Preprint

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An important use of sensors and actuator networks is to comply with health and safety policies in hazardous environments. In order to deal with increasingly large and dynamic environments, and to quickly react to emergencies, tools are needed to simplify the process of translating high-level policies into executable queries and rules. We present a framework to produce such tools, which uses rules to aggregate low-level sensor data, described using the Semantic Sensor Network Ontology, into more useful and actionable abstractions. Using the schema of the underlying data sources as an input, we automatically generate abstractions which are relevant to the use case at hand. In this demonstration we present a policy editor tool and simulation on which policies can be tested.

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SHACL Constraints with Inference Rules

Cited by 12 publications

References 13 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

Towards compliance checking in reified I/O logic via SHACL

A Policy Editor for Semantic Sensor Networks

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