Ontology Transformations Laurent WOUTERS (EADS Innovation Works, France) Marie-Pierre GERVAIS (Université Paris Ouest, LIP6, France)

Motivation: Example Operating a safety-critical system Ontology Transformations 2 EDOC 2012 Activate fuel jettison Check gears are up Flaps to MAX Pitch to 8° Aircraft ditching procedure: Procedure Stress, fatigue, … System Operator

Motivation: Holistic Model-Based Approach to Testing Ontology Transformations 3 EDOC 2012 Execute Results scenario modifications Model Procedure Stress, fatigue, … System Operator

Motivation: Multiple Domain Experts Ontology Transformations 4 EDOC 2012 Model System Engineers Interaction Experts Cognitive Psychologists Procedure Stress, fatigue, … System Operator

Motivation: Multi-View Visual Modeling Ontology Transformations 5 EDOC 2012 System Engineers Interaction Experts Cognitive Psychologists Modeling Environment for Cognitive Psychologists Modeling Environment for Interaction Experts Modeling Environment for System Engineers Domain-Specific Visual Sentences Common Model Artifact xOWL [1] Transformations OWL [1] xOWL: an Executable Modeling Language for Domain Experts, EDOC 2011

State of the Art: Model Transformations Ontology Transformations 6 EDOC 2012 Input common ontologyOutput visual sentences τ ontology to modelmodel to ontology OWL2 World MOF World Translated input modelVisual 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

State of the Art: Ontology Transformations Ontology Transformations 7 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 ontologyOutput visual sentences τ’τ’ OWL2 World MOF World

xOWL Rule Language Rule(:CMAttachSubTree_Activity_route13 Antecedents( 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) ) Ontology Transformations 8 EDOC 2012 OWL2 Axioms Logic Variables

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 Ontology Transformations 9 EDOC 2012 Input ontology Target ontology Meta ontology τ

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 Ontology Transformations 10 EDOC 2012

System Engineers Interaction Experts Cognitive Psychologists Validation: Application to the Use Case (1) Ontology Transformations 11 EDOC 2012

Validation: Application to the Use Case (2) Ontology Transformations 12 EDOC 2012 component instance-of Common Model Artifact

Validation: Application to the Use Case (2) Ontology Transformations 13 EDOC 2012

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 0.99Correlation between 0.90 and 0.99  Less than 1.5sLess than 10ms Ontology Transformations 14 EDOC 2012

Conclusion Ontology Transformations 15 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.