This document discusses using OWL ontologies in closed world applications to validate data integrity. It describes how closed world assumptions (CWA) differ from open world assumptions (OWA) typically used in ontologies, and how CWA are better suited for data validation. It then introduces the idea of integrity constraints (IC), which allow certain OWL axioms to be interpreted under CWA for validation, while other axioms continue with OWA for reasoning. Examples using SKOS and SKOS-XL ontologies are provided to illustrate how ICs can detect violations of data model constraints.

1. Using OWL in
Closed World Applications
Evren Sirin, CTO
Clark & Parsia, LLC
evren@clarkparsia.com

2. Who are we?
• Clark & Parsia is a semantic software startup
– HQ in Washington, DC & office in Boston
• Provides software development and integration
services
• Specializing in Semantic Web, web services, and
advanced AI technologies for federal and
enterprise customers
http://clarkparsia.com/
Twitter: @candp
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3. Some Applications
• Customer and product data
– Find which customer would be interested in buying a
certain product
• System and component descriptions
– Configure components to build a desired system
• Workforce and employee data
– Locate employees with desired expertise
• Patient history and drug data
– Detect and prevent potentially harmful drug interactions
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4. Common Theme
• There is data and lots of it!
• Adding semantics to the data helps a lot
– Some times simple taxonomies, but other times,
complex ontologies
• We have complete knowledge about the domain
• Errors in the data cause problems
– Failures in applications, errors in decision making,
potential loss of revenue, security vulnerabilities, etc.
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5. Data Validation
• Fundamental data management problem
– Verify data integrity and correctness
– Enforce validity of updates
• Relevant in many scenarios
– Storing data for stand-alone applications
– Exchanging data in distributed settings
• Solved (to some degree) in RDBMSs
– Harder to achieve as data semantics increase and/or
more expressive integrity conditions are required
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6. Disclaimer
• Data validity not important for every use case
– Invalid data may be fine for an application
– Invalidity may even be a requirement
• Focus of this talk is cases where data consistency
and integrity are crucial
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7. Roadmap for an App
• How to build one of these applications?
– Represent data as RDF triples
• First step for accomplishing data integration and analysis
– Enrich data with more semantics (RDFS, OWL)
• Infer implicit information from explicit assertions
– Ensure data validity
• Detect errors in the data
– Do something cool with the data
• Obviously...
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8. Reasoning Example
• Input ontology
# Every manager is an employee
Manager subClassOf Employee
# Person0853 is a manager
Person0853 type Manager
• Output inferences
# Person0853 is an employee
Person0853 type Employee

9. Reasoning Example
• Input ontology
# Every manager is an employee
Schema
Manager subClassOf Employee
# Person0853 is a manager
Person0853 type Manager
• Output inferences
# Person0853 is an employee
Person0853 type Employee

10. Reasoning Example
• Input ontology
# Every manager is an employee
Schema
Manager subClassOf Employee
# Person0853 is a manager
Person0853 type Manager Instance data
• Output inferences
# Person0853 is an employee
Person0853 type Employee

11. Validating RDF Data
• Common misunderstanding
– RDFS/OWL is to RDF what XML Schema is to XML
– Describe integrity conditions in RDFS or OWL
• Typing constraints - RDFS domain/range
• Participation constraints - OWL some values restrictions
• Uniqueness constraints - OWL cardinality restriction
– Use a reasoner to find inconsistencies
• Problem: Open World Assumption
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12. Closed vs. Open World
• Two different views on truth:
– CWA: Any statement that is not known to be true is false
– OWA: A statement is false only if it is known to be false
• Used in different contexts
– Databases use CWA because (typically) they contain
complete information
– Ontologies use OWA because (typically) they don't...
that is, they contain incomplete information
• Data validation results significantly different when
using CWA instead of OWA
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13. Typing Constraint
• Only managers can supervise employees
• Input ontology
o supervises domain Manager
o Person085 supervises Person173
OWA CWA
Consistent true false
Infer that Assume that
Reason Person085 type Manager Person085 type not Manager

14. Participation Constraint
• Each supervisor must supervise at least
one employee
• Input axioms
o Supervisor subClassOf supervises some Employee
o Person085 type Supervisor
OWA CWA
Consistent true false
Infer that Assume that
Reason Person085 supervises _:b Person085 supervises _:b
_:b type Employee does not exist

15. Uniqueness Constraint
• Employees can have at most one supervisor
• Input axioms
o supervises InverseFunctional
o Person085 supervises Person173
o Person632 supervises Person173
OWA CWA
Consistent true false
Assume that
Infer that
Reason Person085 sameAs Person632
Person085 sameAs Person632
does not hold

16. Workarounds for CW
• Manually close the world
– Declare all individuals different from each other
– Count existing property values and add a max
cardinality restriction
– Make all disjointness statements explicit and add
negated types to individuals
• Drawbacks
– Can be computationally expensive
– Likely to be error-prone

17. Problem Summary
• Definitions in an OWL schema may have two
purposes
– Infer new statements
– Check if existing statements are valid
• Using OWA for validation is undesirable
– Not always but in many cases
• In a problem domain we may have:
– Complete knowledge about some parts of the domain
– Incomplete knowledge about the other parts

18. Integrity Constraint
Solution
• We defined an alternative semantics for OWL
– Integrity Constraint (IC) semantics use CWA
– Can be combined with regular inference axioms
• Ontology developer chooses which axioms will
be interpreted with...
– OWA - regular OWL axiom, or
– CWA - integrity constraint

19. IC Extension
• Syntax specification
– How do we syntactically say an axiom is an IC and
not a regular OWL axiom?
• Semantics specification
– How do we exactly interpret an IC?
• Validation algorithm
– Given the semantics how do we check for IC
violations?

20. IC Syntax
• Similar approach to using owl:imports
• Define a new annotation property in a new
namespace
Ont1 owl:imports Ont2
Ont1 ic:imports IC1
• Backward compatible, requires minimum change
in tools

21. IC Semantics
• OWL semantics based on model theory
– Similar to First Order Logic
– Formal, precise, and unambiguous
• IC semantics specification
– Extends OWL model theory
– Change couple basic definitions, everything else
follows
• Details published in technical papers
– We are submitting a W3C member submission soon

22. Use Case: SKOS
• Simple Knowledge Organization System (SKOS)
• SKOS provides a model for expressing the basic
structure and content of concept schemes
– Thesauri, classification schemes, subject heading lists,
taxonomies, folksonomies, etc.
• SKOS data model specification
– Informal (Text): http://www.w3.org/TR/skos-reference/
– Formal (OWL): http://www.w3.org/2004/02/skos/core.rdf
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23. SKOS Example
# SKOS reference ontology that contains inference rules
skos:broaderTransitive Transitive skos-reference.ttl
skos:broaderTransitive subPropertyOf skos:broader
# Constraints from SKOS reference expressed as ICs
skos:related propertyDisjointWith skos:broaderTransitive skos-constraints.ttl
# SKOS data that violates the SKOS data model
[] a owl:Ontology ; owl:imports skos-reference.ttl ;
ic:imports skos-constraints.ttl . skos-invalid.ttl
A skos:broader B ; skos:related C .
B skos:broader C .

24. Explanation
VIOLATION: A violates related propertyDisjointWith broaderTransitive
INFERRED: A related C
ASSERTED: A related C
INFERRED: A broaderTransitive C
ASSERTED: A broader B
ASSERTED: B broader C
ASSERTED: broader subPropertyOf broaderTransitive
ASSERTED: broaderTransitive Transitive
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25. Another SKOS Example
# SKOS-XL ontology with a cardinality restriction
skosxl:Label subClassOf skos-xl.ttl
skosxl:literalForm cardinality 1
# SKOS data that violates the SKOS data model
[] a owl:Ontology ; owl:imports skos-xl.ttl .
skos-data.tll
A skosxl:labelRelation LabelA
LabelA type skosxl:Label .
Result: Consistent

26. Another SKOS Example
# SKOS-XL ontology with a cardinality restriction
skosxl:Label subClassOf skos-xl.ttl
skosxl:literalForm cardinality 1
# SKOS data that violates the SKOS data model
[] a owl:Ontology ; owl:imports skos-xl.ttl ;
ic:imports skos-xl.ttl . skos-data.tll
A skosxl:labelRelation LabelA
LabelA type skosxl:Label .
Result: IC Violation

27. Linked Data Application
• Large amounts of instance data
• Validate before publishing/consuming LOD
• Instance data + Inference axioms + Constraints
– Infer new facts using inference axioms with OWA
– Validate data using constraints with CWA
– Inference axioms and constraints are both expressed
in OWL
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28. Validation Algorithm
• An automated translation algorithm
• Automatically maps an OWL IC to ...
– A SPARQL query, or
– A RIF rule
• Many different implementation possibilities
• Off-the-shelf tools can be used for IC validation

29. SPARQL Translation
Supervisor subClassOf supervises some Employee
SELECT * {
?x type Supervisor.
NOT EXISTS {
?x supervises ?y.
?y type Employee.
}
}

30. RIF Translation
Supervisor subClassOf supervises some Employee
Forall ?x ?y (
invalid() :- And (
?x[type -> Supervisor]
Naf And (
?x[supervises -> ?y]
?y[type -> Employee] )))

31. Solution Summary
• Separate ICs from regular OWL ICs
– No new syntax
– Import-based mechanism
• Alternative semantics for ICs
– Extends OWL model theory
– Provides the meanings of ICs formally
• Validation algorithm
– Translate ICs to another formalism
– SPARQL or RIF engines can be used

32. Performance
• Using ICs can improve performance!
• Expressive OWL reasoning is not easy
• Profiles of OWL defined for tractable reasoning
– OWL 2 QL, OWL 2 EL, OWL 2 RL
– Less expressive but more efficient
• Modeling some OWL axioms as ICs may reduce
the overall expressivity
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33. Prototype
• Pellet IC validator
– Translates ICs into SPARQL queries automatically
– Executes SPARQL queries with Pellet
– Query results show constraint violations
– Automatically explain constraint violations
• Free download
– http://clarkparsia.com/pellet/icv
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34. Code Example
// create an inferencing model using Pellet reasoner
InfModel dataModel = ModelFactory.createInfModel(r);
// load the schema and instance data to Pellet
dataModel.read( "file:data.rdf" );
dataModel.read( "file:schema.owl" );
// Create the IC validator and associate it with the dataset
JenaICValidator validator = new JenaICValidator(dataModel);
// Load the constraints into the IC validator
validator.getConstraints().read("file:constraints.owl");
// Get the constraint violations
Iterator<ConstraintViolation> violations =
validator.getViolations();

35. Next Steps
• W3C Member submission for IC semantics
• Robust IC validator implementation
– Incremental validation
– Multi-threaded validation
• Support for IC editing
• Integration with PelletDb
– Scalable reasoning + validation
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36. References
• Evren Sirin, Michael Smith, Evan Wallace
Opening, Closing Worlds - On Integrity Constraints
OWL: Experiences and Directions Workshop
(OWLED '08), October 2008.
• Evren Sirin, Jiao Tao
Towards Integrity Constraints in OWL
OWL: Experiences and Directions Workshop
(OWLED '09), October 2009.
• Jiao Tao, Evren Sirin, Jie Bao, Deborah L. McGuinness
Integrity Constraints in OWL
To AppearThe 24th AAAIConference on Artificial
Intelligence (AAAI '10), July 2010.

37. Questions