Towards an RDF Validation Language based on Regular Expression Derivatives Author: Jose Emilio Labra Gayo Slides presented at: Linked Web Data Management Workshop Brussels, 27th March, 2015

1. Towards an RDF Validation Language
based on Regular Expression Derivatives
Eric Prud'hommeaux
World Wide Web
Consortium
MIT, Cambridge, MA, USA
Harold Solbrig
Mayo Clinic
USA
College of Medicine, Rochester,
MN, USA
Jose Emilio Labra Gayo
WESO Research group
University of Oviedo
Spain
Sławek Staworko
LINKS, INRIA & CNRS
University of Lille, France

2. Overview
Shape Expressions for RDF validation - Justification
Regular Shape Expressions
Axiomatic Semantics
Implementation based on Derivatives
Regular Shape Expression Schemas
Adapt Axiomatic Semantics to Schemas
Adapt Implementation based on Derivatives
Conclusions & Future work

3. Shape Expressions
Simple and intuitive language that can:
Describe the topology of RDF data
Validate that RDF instance data matches a shape
Two syntaxes
Compact syntax (inspired by RelaxNG, Turtle and SPARQL)
RDF
Related to W3c RDF Data Shapes Working Group

4. Example: RDF model of a Person
Person__
foaf:age xsd:integer
foaf:name xsd:string +
0..*
foaf:knows
:john foaf:age 23;
foaf:name "John";
foaf:knows :bob .
:bob foaf:age 34;
foaf:name "Bob", "Robert" .


<Person> {
foaf:age xsd:integer
, foaf:name xsd:string+
, foaf:knows @<Person>*
}
Shape Expressions Schema
Some RDF data
:mary foaf:age 50, 65 .
E-R Diagram

5. Why not SPARQL?
<Person> {
foaf:age xsd:integer
, foaf:name xsd:string+
, foaf:knows @<Person>*
}ASK { { SELECT ?Person {
?Person foaf:age ?o .
} GROUP BY ?Person HAVING (COUNT(*)=1)
}
{ SELECT ?Person {
?Person foaf:age ?o .
FILTER ( isLiteral(?o) &&
datatype(?o) = xsd:integer )
} GROUP BY ?Person HAVING (COUNT(*)=1)
}
...
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...
...
{ SELECT ?Person (COUNT(*) AS ?Person_c0) {
?Person foaf:name ?o .
} GROUP BY ?Person HAVING (COUNT(*)>=1)
}
{ SELECT ?Person (COUNT(*) AS ?Person_c1) {
?Person foaf:name ?o .
FILTER (isLiteral(?o) &&
datatype(?o) = xsd:string)
} GROUP BY ?Person HAVING (COUNT(*)>=1) }
FILTER (?Person_c0 = ?Person_c1)
...
...
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...
...
{ { { SELECT ?Person (COUNT(*) AS ?Person_c2) {
?Person foaf:knows ?o .
} GROUP BY ?Person }
{ SELECT ?Person (COUNT(*) AS ?Person_c3) {
?Person foaf:knows ?o .
FILTER ((isIRI(?o) || isBlank(?o)))
} GROUP BY ?Person HAVING (COUNT(*) >= 1)
}
FILTER (?Person_c2 = ?Person_c3)
}
...
...
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...
...
UNION {
SELECT ?Person {
OPTIONAL { ?Person foaf:knows ?o }
FILTER (!bound(?o))
}
}
}
}
...
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6. Regular Shape Expressions (RSEs)
Simplified version of Shape Expressions
Based on Regular Expressions
Sets of triples instead of list of characters
Interleave instead of concatenation
Abstract syntax

7. Shape Expressions vs RSEs*
<Shape1> {
foaf:age xsd:integer
, foaf:name xsd:string*
}
Example1:
Shape Expression RSE
* Note: We are considering a subset of Shape Expressions with Closed Shapes, and inclusive Or
<Shape2> {
:a ( 1 )
, :b ( 1 2 ) *
}
Example 2:

8. Cardinalities in RSEs
Cardinalities can be defined as:
Example:

9. Shape of a RSE:
Example

10. Simplification rules
It is easy to show that the operators obey:

11. Matching triples with RSEs

12. Example matching tree
Rules employed

13. Derivatives of RSEs
Brzozowski's algorithm (1964) developed for Regular Expressions
We adapted that algorithm to RSEs
Calculates the derivative of a RSE with respect to a triple t:
Definition:

14. Calculating the derivative Definitions
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15. Matching using derivatives
Auxiliary function that returns true if a RSE matches the empty graph
The matching relation can be expressed as:

16. Example trace:
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17. Regular Shape Expression Schemas
Given a set of labels, a RSE schema is a function
where we extend RSEs to admit label references
Example 1:
Example 2:
<Person> {
foaf:age xsd:integer
, foaf:knows @<Person>*
}
Corresponds to:

18. From matching to typing
We extend previous definitions to include the notion of typing
A typing associates a label to a node in a context
Definitions on typings
The matching algorithm returns the typing in the context:

19. Matching RSEs Schemas
We define the matching of a RSE e with a set of triples as a partial
function that returns a typing.
The function takes a typing context as argument
and we extend previous axiomatic definitions as...

20. Axiomatic definitions adapted RSE Schemas

21. Derivative of a RSE in a typing context
We adapt previous definitions to typing contexts
where

22. Example:

23. Implementations
The algorithm has been implemented in Scala
Available at: http://labra.github.io/shexcala
We have also implemented a simplified prototype following the paper
definitions in Haskell
Available at: http://labra.github.io/Haws
An online version is also available at: http://rdfshape.weso.es

24. First experimental results
Comparison between derivatives (deriv) and backtracking (back)

25. Conclusions & Future work
Declarative algorithm to match Regular Shape Expressions
Based on equational reasoning
Theoretical complexity is unaffected
However, the derivatives algorithm behaves better than backtracking in practice
Future work:
Prove the correctness of the algorithm
Experimental results
Align this work with current RDF Data Shapes development

26. End of Presentation

27. SHACL vs RSEs
At this moment, SHACL is being defined by the RDF Data Shapes WG
Some differences:
Open Shapes (allow remaining triples)
Arcs check that there are no other arcs with the same predicate and different
values
And operator instead of interleave
Inclusive vs Exclusive-or
Semantics of all these features is under discussion

28. Example of derivatives that don't match