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Enhancing Safety-Critical Systems through Ontology Transformations: A Holistic Approach

This paper discusses the application of ontology transformations in safety-critical systems, emphasizing a holistic, model-based approach to testing that incorporates stress, fatigue, and procedural factors affecting operators. We highlight collaboration among various domain experts, including cognitive psychologists and system engineers, to model complex scenarios in aircraft ditching procedures. Our innovative xOWL rule language enables live incremental transformations and validation of ontology frameworks, ensuring rapid performance while accommodating different expert perspectives and supporting scenario modifications.

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Enhancing Safety-Critical Systems through Ontology Transformations: A Holistic Approach

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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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