Ontology Transformations Laurent WOUTERS (EADS Innovation Works, France) Marie-Pierre GERVAIS...

Ontology Transformations

Laurent WOUTERS (EADS Innovation Works, France)

Marie-Pierre GERVAIS (Université Paris Ouest, LIP6, France)


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Motivation: Example

Operating a safety-critical system

EDOC 2012

Activate fuel jettison

Check gears are up

Flaps to MAX Pitch to 8°

Aircraft ditching procedure:

Procedure

Stress, fatigue, …

System

Operator


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Motivation: Holistic Model-Based Approach to Testing

EDOC 2012

Execute

Results

scenariomodifications

Mod

el

Procedure


System

Operator


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Motivation: Multiple Domain Experts

EDOC 2012

Mod

el

System EngineersInteraction ExpertsCognitive Psychologists

Procedure


System

Operator


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Motivation: Multi-View Visual Modeling

EDOC 2012

System EngineersInteraction ExpertsCognitive Psychologists

Modeling Environment for Cognitive Psychologists

Modeling Environment for Interaction Experts

Modeling Environment for System Engineers

Domain-Specific Visual Sentences



Common Model ArtifactxOWL

[1]

Transformations

OWL

[1] xOWL: an Executable Modeling Language for Domain Experts, EDOC 2011


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State of the Art: Model Transformations

EDOC 2012

Input common ontology Output visual sentences

τontology to model model to ontology

OWL2 World

MOF WorldTranslated input model Visual sentences model

Query/View/Transform [1] (SmartQVT, mediniQVT, ModelMorf) ATLAS Transformation Language [2] Triple Graph Grammars [3]

[1] OMG, Meta Object Facility Query/View/Transformation version1.1, 2011[2] Jouaultand, Kurtev, Transforming Models with ATL MoDELS 2006[3] Greenyer, Kindler, Comparing Relational Model Transformation Technologies, SoSyM 2010[4] Silva Parreiras, Staab, Using Ontologies with UML Class-Based Modeling: The Two Use Approach Data & Knowledge Engineering 2010[5] Djuric, Gasevic, Devedzic, Ontology Modeling and MDA, Journal of Object Technology 2005

Cannot map the whole semantic of OWL [4,5]

ODM


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State of the Art: Ontology Transformations

EDOC 2012

[6] W3C, SWRL: A Semantic Web Rule Language Combining OWL and RuleML, 2010[7] Horrockse et al., OWL Rules: a Proposal and Prototype Implementation,Web Semantics: Science, Services and Agents on the World Wide Web 2005

Cannot operate over classes and relations [7]

Semantic Web Rule Language [6]

Input common ontology Output visual sentencesτ’

OWL2 World

MOF World


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xOWL Rule Language

Rule(:CMAttachSubTree_Activity_route13Antecedents(

ClassAssertion(command:Attach ?com)ObjectPropertyAssertion(command:symbol ?com

view:Activity)ObjectPropertyAssertion(command:parent ?com ?np)ObjectPropertyAssertion(command:child ?com ?nc)Meta(ObjectPropertyAssertion(view:route13 ?nr ?np))Meta(ObjectPropertyAssertion(meta:trace ?nr ?or))Meta(ObjectPropertyAssertion(meta:trace ?nc ?oc))

)Consequents(

ClassAssertion(?oc ?or))

) EDOC 2012

OWL2 Axioms

Logic Variables

1 rule = antecedents and consequents (patterns of OWL2 axioms) Logic variables can be used wherever ontological entities or literal can be

expected Negative antecedents and consequents Negative conjunctive antecedents () Guards (conditions)


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xOWL Transformations

A transformation = set of independent xOWL rules (no prioritization)

Positive consequents are added to the target

Negative consequents are removed from the target

A “Meta” ontology is used to store traceability information

“Meta” antecedents are matched in the meta ontology

“Meta” consequents are added or removed from it

EDOC 2012

Input ontology Target ontology

Meta ontology

τ


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Validation

3 Steps:

Implementation

Demonstration on the use case

Performance study

Implementation: Incremental transformation engine

The RETE pattern-matching algorithm is used for matching rules’ antecedents

Available under the LGPL license at http://xowl.org.

EDOC 2012


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System Engineers

Interaction Experts

Cognitive Psychologists

Validation: Application to the Use Case (1)

EDOC 2012


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EDOC 2012

component

instance-of

Common Model Artifact


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System Engineers

Interaction Experts

Cognitive Psychologists


EDOC 2012

component

instance-of

Common Model Artifact


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Validation: Performance Study

Objective: Ensure that ontology transformations have sufficient performances for live incremental transformations

Tested the transformations from the use case with ontologies of increasing sizes

Correlation is 0.99 Correlation between 0.90 and 0.99

Less than 1.5s Less than 10ms

EDOC 2012

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 200000

200

400

600

800

1000

1200

1400

1600

Cockpit HierarchyCockpit State-MachinePilot's ProceduresCognitive Processes

Input ontology size (in number of axioms)

Init

ial

Tra

nsf

orm

atio

n t

ime

(ms)

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 200000

1

2

3

4

5

6

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Cockpit HierarchyCockpit State-MachinePilot's ProceduresCognitive Processes

Input ontology size (in number of axioms)

Incr

emen

t T

ran

sfo

rmat

ion

tim

e (m

s)


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Conclusion

EDOC 2012

Express ontology transformations with the xOWL Rule Language

Execute live incremental ontology transformations

Applied to the use case:

Supports multiple domain-specific perspectives on a common model artifact

Improves the safety of critical systems

Perspectives:

More expressive rule language with explicit rules prioritization for example.

Support the software engineers that have to write the transformations with visual notations for rules.

Ontology Transformations Laurent WOUTERS (EADS Innovation Works, France) Marie-Pierre GERVAIS...

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