RR2010 Keynote

Data Validation with
OWL Integrity Constraints

Evren Sirin, CTO
Clark & Parsia, LLC
evren@clarkparsia.com

1
Wednesday, September 22, 2010

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
2


Overview
• Data validation with OWL
– Representation and validation of integrity constraints
• Use cases
– Examples, issues, workarounds
• OWL Integrity Constraints
– Syntax, semantics, validation
• Comparison with other approaches
– Epistemic DLs, Epistemic QLs, Rules
• Implementation and performance
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

4


Common Theme
• There is data and lots of it!
• Adding semantics to the data helps a lot
– Sometimes 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.

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
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

7


Building Semantic Apps
• 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...
8


Reasoning Example
• Input ontology
# Every supervisor is an employee
Supervisor subClassOf Employee
# Person0853 is a manager
Person085 type Supervisor
• Output inferences
# Person0853 is an employee
Person085 type Employee

9


Reasoning Example
• Input ontology
Schema
Person085 type Supervisor

9


Reasoning Example
• Input ontology
Schema
Person085 type Supervisor Instance data

9


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

10


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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Typing Constraint
• Only managers can supervise employees
• Input ontology
o supervises domain Supervisor
o Person085 supervises Person173

OWA CWA
!Consistent true false
Infer that Assume that
!Reason Person085 type Supervisor Person085 type not Supervisor

12


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

13


Uniqueness Constraint
• Employees can have at most one supervisor
• Input axioms
o supervises InverseFunctional

OWA CWA
Consistent true false
Assume that
Infer that
Reason Person085 sameAs Person632
Person085 sameAs Person632
does not hold

14


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

15


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
16


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

17


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?

18


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
19


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

20


SKOS Example
# SKOS reference ontology that contains inference rules
skos:broaderTransitive Transitive skos-reference.ttl
skos:broaderTransitive subPropertyOf skos:broader

# NL constraints from SKOS specification 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 .

IC validation requires OWL reasoning 21


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 when
# SKOS ontology is imported as ICs as well
[] a owl:Ontology ; owl:imports skos-xl.ttl ;
ic:imports skos-xl.ttl . skos-invalid.tll

A skosxl:labelRelation LabelA
LabelA type skosxl:Label .

Same ontology can be both
a regular OWL import and an IC import 22


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
23


IC Interpretations
• A regular OWL interpretation I = ( ΔI, ΔD, ⋅C, ⋅OP, ⋅DP, ⋅I,
⋅DT, ⋅LT, ⋅FA) is a 9-tuple
– ⋅C is the class interpretation function that assigns to each class
C ∈ VC a subset (C)C ⊆ ΔI
• An OWL IC interpretation Γ = ( ΔI, ΔD, I, U, ⋅C, ⋅OP, ⋅DP, ⋅I,
⋅DT, ⋅LT, ⋅FA) is a 11-tuple where I and elements of U are
regular OWL interpretations
– (C)C = {xI | x ∈ VI and for each Uj ∈ U we have that xIUj
∈ (C)Cj }.
• More details available in references at the end
24


Other Approaches
• IC semantics of Motik et al. [WWW2007]
• Epistemic DLs
• Epistemic QLs
• Rules with negation as failure operators

25


Validation Algorithm
• An automated translation!algorithm
• Automatically maps an OWL IC to a SPARQL
query
– Query must be evaluated with OWL entailment regime
• ICs can be mapped to RIF rules too
– Use SPARQL and Datalog correspondence
– RIF deﬁnes Negation-as-Failure operator
• Many different implementation possibilities
• Off-the-shelf tools can be used for IC validation
26


SPARQL Translation
Supervisor subClassOf supervises some Employee

SELECT * {
?x type Supervisor.
NOT EXISTS {
?x supervises ?y.
?y type Employee.
}
}

27


RIF Translation
Supervisor subClassOf supervises some Employee

Forall ?x ?y (
invalid() :- And (
?x[type -> Supervisor]
Naf And (
?x[supervises -> ?y]
?y[type -> Employee] )))

28


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
29


Explanations
• Explanations for positive atoms well-understood
– Smallest subset of the ontology that entails the atom
– Precise & laconic explanation
– Lemma generation
• Explanation for IC violation are tricky
– Need to explain negation (i.e. missing values)
– Lemma generation even more crucial
• Simple solution
– Explanation represented as a tree where each node
represents an existing or missing axiom
30


Explanation Example (1)
VIOLATION: A violates related propertyDisjointWith broaderTransitive
INFERRED: A related C
INFERRED: A broaderTransitive C

31


ASSERTED: A related C

32


ASSERTED: A related C
ASSERTED: A broader B
ASSERTED: B broader C
ASSERTED: broader subPropertyOf broaderTransitive
ASSERTED: broaderTransitive Transitive

33


VIOLATION: A violates Label subClassOf literalForm cardinality 1
INFERRED: A type Label
INFERRED: A labelRelation LabelA
NOT INFERRED: LabelA literalForm

Missing values are represented as
34


VIOLATION: A violates Label subClassOf literalForm cardinality 1
INFERRED: A type Label
ASSERTED: A type Label
INFERRED: A labelRelation LabelA
ASSERTED: A labelRelation LabelA
NOT INFERRED: LabelA literalForm
NOT ASSERTED: LabelA literalForm

35


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 expressivity where OWL reasoning is used

36


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

37


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();

38


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

39


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.

40


Questions

41


Other Approaches
• IC semantics of Motik et al. [WWW2007]
• Epistemic DLs
• Epistemic QLs
• Rules

42


IC Semantics of Motik et al.
• Same motivation and similar approach
– Separate ICs for regular OWL axioms
– Use ICs for validation only
• Semantics based on outer skolemization on ﬁrst order
formula and entailment in minimal Herbrand models
• Several features of the semantics made it unsuitable
for us
– ICs can be satisﬁed by existential variables
– Disjunction can cause false positives

43


Epistemic DLs
• DLs extended with epistemic operator K
• ICs can be represented as epistemic queries over a
regular KB
– Aligns with Reiter’s original characterization of ICs
• Example:
– KSupervisor subClassOf Ksupervises some KEmployee
• Only major differences
– We are not using K operator explicitly
– No Unique Name Assumption in OWL ICs
44


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