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RDF Validation tutorial
ShEx/SHACL by example
Eric Prud'hommeaux
World Wide Web, USA
Harold Solbrig
Mayo Clinic, USA
Jose Emilio Labra Gayo
WESO Research group
Spain
Iovka Boneva
LINKS, INRIA & CNRS, France
Contents
Overview of RDF data model
Motivation for RDF Validation and previous approaches
ShEx by example
SHACL by example
ShEx vs SHACL
RDF Data Model
Overview of RDF Data Model and simple exercise
Link to slides about
RDF Data Model
http://www.slideshare.net/jelabra/rdf-data-model
RDF, the good parts...
RDF as an integration language
RDF as a lingua franca for semantic web and linked data
RDF data stores & SPARQL
RDF flexibility
Data can be adapted to multiple environments
Open and reusable data by default
RDF, the other parts
Inference & knowledge representation
RDF should combine well with KR vocabularies (RDF Schema, OWL...)
Performance of RDF based systems with inference = challenging
Consuming & producing RDF
Multiple serializations: Turtle, RDF/XML, JSON-LD, ...
Embedding RDF in HTML
Describing and validating RDF content
Why describe & validate RDF?
For RDF producers
Developers can understand the contents they are going to produce
They can ensure they produce the expected structure
Advertise the structure
Generate interfaces
For RDF consumers
Understand the contents
Verify the structure before processing it
Query generation & optimization
Similar technologies
Technology Schema
Relational Databases DDL
XML DTD, XML Schema, RelaxNG
Json Json Schema
RDF ?
Our goal is to fill that gap
Understanding the problem
RDF is composed by nodes and arcs between nodes
We can describe/check
form of the node itself (node constraint)
number of possible arcs incoming/outgoing from a node
possible values associated with those arcs
:alice schema:name "Alice";
schema:knows :bob .
IRI schema:name string (1, 1) ;
schema:knows IRI (0, *)
RDF Node
Shape of RDF
Nodes that
represent Users
<User> IRI {
schema:name xsd:string ;
schema:knows IRI *
}
ShEx
Understanding the problem
RDF validation ≠ ontology definition ≠ instance data
Ontologies are usually focused on real world entities
RDF validation is focused on RDF graph features (lower level)
Ontology
Constraints
RDF Validation
Instance data
Different levels
:alice schema:name "Alice";
schema:knows :bob .
<User> IRI {
schema:name xsd:string ;
schema:knows IRI
}
schema:knows a owl:ObjectProperty ;
rdfs:domain schema:Person ;
rdfs:range schema:Person .
A user must have only two properties:
schema:name of value xsd:string
schema:knows with an IRI value
Understanding the problem
Shapes ≠ types
Nodes in RDF graphs can have zero, one or many rdf:type arcs
One type can be used for multiple purposes (foaf:Person)
Data doesn't need to be annotated with fully discriminating types
foaf:Person can represent friend, invitee, patient,...
Different meanings and different structure depending on the context
We should be able to define specific validation constraints in different contexts
Understanding the problem
RDF flexibility
Mixed use of objects & literals
schema:creator can be a string or schema:Person in the same data
:angie schema:creator "Keith Richards" ,
[ a schema:Person ;
schema:singleName "Mick" ;
schema:lastName "Jagger"
] .
See other examples from http://schema.org
Understanding the problem
Repeated properties
Sometimes, the same property is used for different purposes in the
same data
Example: A book record must have 2 codes with different structure
:book schema:productID "isbn:123-456-789";
schema:productID "code456" .
A practical example from FHIR
See: http://hl7-fhir.github.io/observation-example-bloodpressure.ttl.html
Previous RDF validation approaches
SPARQL based
Plain SPARQL
SPIN: http://spinrdf.org/
OWL based
Stardog ICV
http://docs.stardog.com/icv/icv-specification.html
Grammar based
OSLC Resource Shapes
https://www.w3.org/Submission/2014/SUBM-shapes-20140211/
Use SPARQL queries to detect errors
Pros:
Expressive
Ubiquitous
Cons
Expressive
Idiomatic - many ways to encode
the same constraint
ASK {{ SELECT ?Person {
?Person schema:name ?o .
} GROUP BY ?Person HAVING (COUNT(*)=1)
}
{ SELECT ?Person {
?Person schema:name ?o .
FILTER ( isLiteral(?o) &&
datatype(?o) = xsd:string )
} GROUP BY ?Person HAVING (COUNT(*)=1)
}
{ SELECT ?Person (COUNT(*) AS ?c1) {
?Person schema:gender ?o .
} GROUP BY ?Person HAVING (COUNT(*)=1)}
{ SELECT ?Person (COUNT(*) AS ?c2) {
?S schema:gender ?o .
FILTER ((?o = schema:Female ||
?o = schema:Male))
} GROUP BY ?Person HAVING (COUNT(*)=1)}
FILTER (?c1 = ?c2)
}
Example:
schema:name must be a xsd:string
schema:gender must be schema:Male or schema:Female
SPIN
SPARQL inferencing notation http://spinrdf.org/
Developed by TopQuadrant
Commercial product
Vocabulary associated with user-defined functions in SPARQL
SPIN has influenced SHACL (see later)
Stardog ICV
ICV - Integrity Constraint Validation
Commercial product
OWL with unique name assumption and closed world
Compiled to SPARQL
More info: http://docs.stardog.com/icv/icv-specification.html
OSLC Resource Shapes
OSLC Resource Shapes
https://www.w3.org/Submission/shapes/
Grammar based approach
Language for RDF validation
Less expressive than ShEx
:user a rs:ResourceShape ;
rs:property [
rs:name "name" ;
rs:propertyDefinition schema:name ;
rs:valueType xsd:string ;
rs:occurs rs:Exactly-one ;
] ;
rs:property [
rs:name "gender" ;
rs:propertyDefinition schema:gender ;
rs:allowedValue schema:Male, schema:Female ;
rs:occurs rs:Zero-or-one ;
].
Other approaches
Dublin Core Application profiles (K. Coyle, T. Baker)
http://dublincore.org/documents/dc-dsp/
RDF Data Descriptions (Fischer et al)
http://ceur-ws.org/Vol-1330/paper-33.pdf
RDFUnit (D. Kontokostas)
http://aksw.org/Projects/RDFUnit.html
...
ShEx and SHACL
2013 RDF Validation Workshop
Conclusions of the workshop:
There is a need of a higher level, concise language for RDF Validation
ShEx initially proposed by Eric Prud'hommeaux
2014 W3c Data Shapes WG chartered
2015 SHACL as a deliverable from the WG
Continue this tutorial with...
ShEx by example
SHACL by example
ShEx vs SHACL
Future work and
applications
http://www.slideshare.net/jelabra/shex-by-example
http://www.slideshare.net/jelabra/shacl-by-example
http://www.slideshare.net/jelabra/shex-vs-shacl
http://www.slideshare.net/jelabra/rdf-validation-future-work-and-applications

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RDF validation tutorial

  • 1. RDF Validation tutorial ShEx/SHACL by example Eric Prud'hommeaux World Wide Web, USA Harold Solbrig Mayo Clinic, USA Jose Emilio Labra Gayo WESO Research group Spain Iovka Boneva LINKS, INRIA & CNRS, France
  • 2. Contents Overview of RDF data model Motivation for RDF Validation and previous approaches ShEx by example SHACL by example ShEx vs SHACL
  • 3. RDF Data Model Overview of RDF Data Model and simple exercise Link to slides about RDF Data Model http://www.slideshare.net/jelabra/rdf-data-model
  • 4. RDF, the good parts... RDF as an integration language RDF as a lingua franca for semantic web and linked data RDF data stores & SPARQL RDF flexibility Data can be adapted to multiple environments Open and reusable data by default
  • 5. RDF, the other parts Inference & knowledge representation RDF should combine well with KR vocabularies (RDF Schema, OWL...) Performance of RDF based systems with inference = challenging Consuming & producing RDF Multiple serializations: Turtle, RDF/XML, JSON-LD, ... Embedding RDF in HTML Describing and validating RDF content
  • 6. Why describe & validate RDF? For RDF producers Developers can understand the contents they are going to produce They can ensure they produce the expected structure Advertise the structure Generate interfaces For RDF consumers Understand the contents Verify the structure before processing it Query generation & optimization
  • 7. Similar technologies Technology Schema Relational Databases DDL XML DTD, XML Schema, RelaxNG Json Json Schema RDF ? Our goal is to fill that gap
  • 8. Understanding the problem RDF is composed by nodes and arcs between nodes We can describe/check form of the node itself (node constraint) number of possible arcs incoming/outgoing from a node possible values associated with those arcs :alice schema:name "Alice"; schema:knows :bob . IRI schema:name string (1, 1) ; schema:knows IRI (0, *) RDF Node Shape of RDF Nodes that represent Users <User> IRI { schema:name xsd:string ; schema:knows IRI * } ShEx
  • 9. Understanding the problem RDF validation ≠ ontology definition ≠ instance data Ontologies are usually focused on real world entities RDF validation is focused on RDF graph features (lower level) Ontology Constraints RDF Validation Instance data Different levels :alice schema:name "Alice"; schema:knows :bob . <User> IRI { schema:name xsd:string ; schema:knows IRI } schema:knows a owl:ObjectProperty ; rdfs:domain schema:Person ; rdfs:range schema:Person . A user must have only two properties: schema:name of value xsd:string schema:knows with an IRI value
  • 10. Understanding the problem Shapes ≠ types Nodes in RDF graphs can have zero, one or many rdf:type arcs One type can be used for multiple purposes (foaf:Person) Data doesn't need to be annotated with fully discriminating types foaf:Person can represent friend, invitee, patient,... Different meanings and different structure depending on the context We should be able to define specific validation constraints in different contexts
  • 11. Understanding the problem RDF flexibility Mixed use of objects & literals schema:creator can be a string or schema:Person in the same data :angie schema:creator "Keith Richards" , [ a schema:Person ; schema:singleName "Mick" ; schema:lastName "Jagger" ] . See other examples from http://schema.org
  • 12. Understanding the problem Repeated properties Sometimes, the same property is used for different purposes in the same data Example: A book record must have 2 codes with different structure :book schema:productID "isbn:123-456-789"; schema:productID "code456" . A practical example from FHIR See: http://hl7-fhir.github.io/observation-example-bloodpressure.ttl.html
  • 13. Previous RDF validation approaches SPARQL based Plain SPARQL SPIN: http://spinrdf.org/ OWL based Stardog ICV http://docs.stardog.com/icv/icv-specification.html Grammar based OSLC Resource Shapes https://www.w3.org/Submission/2014/SUBM-shapes-20140211/
  • 14. Use SPARQL queries to detect errors Pros: Expressive Ubiquitous Cons Expressive Idiomatic - many ways to encode the same constraint ASK {{ SELECT ?Person { ?Person schema:name ?o . } GROUP BY ?Person HAVING (COUNT(*)=1) } { SELECT ?Person { ?Person schema:name ?o . FILTER ( isLiteral(?o) && datatype(?o) = xsd:string ) } GROUP BY ?Person HAVING (COUNT(*)=1) } { SELECT ?Person (COUNT(*) AS ?c1) { ?Person schema:gender ?o . } GROUP BY ?Person HAVING (COUNT(*)=1)} { SELECT ?Person (COUNT(*) AS ?c2) { ?S schema:gender ?o . FILTER ((?o = schema:Female || ?o = schema:Male)) } GROUP BY ?Person HAVING (COUNT(*)=1)} FILTER (?c1 = ?c2) } Example: schema:name must be a xsd:string schema:gender must be schema:Male or schema:Female
  • 15. SPIN SPARQL inferencing notation http://spinrdf.org/ Developed by TopQuadrant Commercial product Vocabulary associated with user-defined functions in SPARQL SPIN has influenced SHACL (see later)
  • 16. Stardog ICV ICV - Integrity Constraint Validation Commercial product OWL with unique name assumption and closed world Compiled to SPARQL More info: http://docs.stardog.com/icv/icv-specification.html
  • 17. OSLC Resource Shapes OSLC Resource Shapes https://www.w3.org/Submission/shapes/ Grammar based approach Language for RDF validation Less expressive than ShEx :user a rs:ResourceShape ; rs:property [ rs:name "name" ; rs:propertyDefinition schema:name ; rs:valueType xsd:string ; rs:occurs rs:Exactly-one ; ] ; rs:property [ rs:name "gender" ; rs:propertyDefinition schema:gender ; rs:allowedValue schema:Male, schema:Female ; rs:occurs rs:Zero-or-one ; ].
  • 18. Other approaches Dublin Core Application profiles (K. Coyle, T. Baker) http://dublincore.org/documents/dc-dsp/ RDF Data Descriptions (Fischer et al) http://ceur-ws.org/Vol-1330/paper-33.pdf RDFUnit (D. Kontokostas) http://aksw.org/Projects/RDFUnit.html ...
  • 19. ShEx and SHACL 2013 RDF Validation Workshop Conclusions of the workshop: There is a need of a higher level, concise language for RDF Validation ShEx initially proposed by Eric Prud'hommeaux 2014 W3c Data Shapes WG chartered 2015 SHACL as a deliverable from the WG
  • 20. Continue this tutorial with... ShEx by example SHACL by example ShEx vs SHACL Future work and applications http://www.slideshare.net/jelabra/shex-by-example http://www.slideshare.net/jelabra/shacl-by-example http://www.slideshare.net/jelabra/shex-vs-shacl http://www.slideshare.net/jelabra/rdf-validation-future-work-and-applications