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Ontology Transformations

Ontology Transformations. Laurent WOUTERS (EADS Innovation Works, France) Marie-Pierre GERVAIS ( Université Paris Ouest , LIP6, France). Motivation: Example. Operating a safety-critical system. Stress, fatigue, …. Procedure. Operator. System. Aircraft ditching procedure :.

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Ontology Transformations

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

  2. Ontology Transformations Motivation: Example • Operating a safety-critical system Stress, fatigue, … Procedure Operator System Aircraftditchingprocedure:

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

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

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

  6. Ontology Transformations State of the Art: Model Transformations • Query/View/Transform [1] (SmartQVT, mediniQVT, ModelMorf) • ATLAS Transformation Language [2] • Triple Graph Grammars [3] Translated input model Visual sentences model τ MOF World Cannotmap the wholesemantic of OWL [4,5] ontology to model model to ontology OWL2 World Input commonontology Output visual sentences ODM [1] OMG, Meta Object FacilityQuery/View/Transformation version1.1, 2011 [2] Jouaultand, Kurtev, TransformingModelswith ATLMoDELS 2006 [3] Greenyer, Kindler, ComparingRelational Model Transformation Technologies, SoSyM 2010 [4] Silva Parreiras, Staab, Using Ontologies with UML Class-BasedModeling: The Two Use Approach Data & Knowledge Engineering 2010 [5] Djuric, Gasevic, Devedzic, OntologyModeling and MDA, Journal of Object Technology2005

  7. Ontology Transformations State of the Art: Ontology Transformations • Semantic Web RuleLanguage [6] Cannotoperate over classes and relations [7] MOF World OWL2 World τ’ Input commonontology Output visual sentences [6] W3C, SWRL: A Semantic Web RuleLanguageCombining 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

  8. Ontology Transformations xOWL RuleLanguage • 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) • Rule(:CMAttachSubTree_Activity_route13 • Antecedents( • ClassAssertion(command:Attach?com) • ObjectPropertyAssertion(command:symbol?comview: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) • ) • ) OWL2 Axioms Logic Variables

  9. Ontology Transformations xOWL Transformations • A transformation = set of independent xOWL rules (no prioritization) • Positive consequents are added to the target • Negativeconsequents are removedfrom 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 Meta ontology τ Input ontology Target ontology

  10. Ontology Transformations 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.

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

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

  13. Ontology Transformations Validation: Application to the Use Case(2) Interaction Experts Common Model Artifact component System Engineers instance-of Cognitive Psychologists

  14. Ontology Transformations 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

  15. Ontology Transformations Conclusion • 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.

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