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Ontology in Model-Based Systems Engineering

Ontology in Model-Based Systems Engineering. Henson Graves 29 January 2011. Preview Of Monday Discussion Topics. SysML and ontology in biomedical modeling Ontology reuse in MBSE Ontology for identifying and resolving model ambiguity

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Ontology in Model-Based Systems Engineering

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  1. Ontology in Model-Based Systems Engineering Henson Graves 29 January 2011

  2. Preview Of Monday Discussion Topics SysML and ontology in biomedical modeling Ontology reuse in MBSE Ontology for identifying and resolving model ambiguity Approaches to integrating SysML with logic based frameworks, e.g., OWL Overlap with other MBSE working groups Enterprise modeling (DoDAF, … MBSE standards Issues, questions, what else is going on,…

  3. SysML and Ontology in Biomedical Modeling Henson Graves Yvonne Bijan 30 January 2011

  4. Human Heart Use Case For Structural Modeling Focus of conceptual modeling is on structure

  5. Conceptual Modeling Objectives Construct a model that captures what is common to all (or at least) most human hearts corresponds to product model, or product line Perform general reasoning about effects of pathology and disease symptom propagation general properties of operation Use general case to analyze and reason about a specific heart fault detection

  6. MBSE Interest Do the modeling principles used by Description Logic (OWL) community offer anything for MBSE? Will these examples and the DL models help us understand how to integrate formal reasoning with SysML? How do biomedical examples look in SysML? Do the modeling principles used for air vehicles and other systems work in biomedical domain?

  7. Ontology Reuse In MBSE Henson Graves January 2011

  8. Ontology Applications in MBSE Examples Units and measures Physical interactions (laws) Material classification and properties Enterprise concepts Levels of rigor Vocabulary Informal textual semantics of vocabulary Formal (axiomatic) semantics

  9. Example Opportunity: Analysis Result

  10. Approaches To Achieving Ontology Reuse • Identify candidate ontologies, acquire and make available • Start over with uniform modeling principles

  11. Common MBSE Modeling Questions and How Ontology Helps Henson Graves Conrad Bock January 10, 2011

  12. What Does a Model Describe Are the things described by a model all the same, or can they be different? In particular, do they all have the same parts linked together in the same way? The answer is no (perhaps intentionally) if The model is incomplete. Does a car have more than engines and wheels? Are there any specializations of the model? Is there more than one model of the same car? The model is complete, but isn’t specific. What kind of engine? What kind of wheel? Which goes with which? Parts aren’t distinguished or equated. Does the car roll on the driven wheels?

  13. Ontology Languages Ontology languages enable modelers to say how they want these questions to be answered Doesn’t mean system engineers need to learn ontology languages Ontology languages can motivate and validate extensions to SysML/UML and other modeling languages to address ambiguities Improves quality of communication between people, between people and machines, and between machines Giving a model a descriptive name (“complete car model”) does not mean that people or machines know exactly what you are talking about.

  14. Integrating SysML with OWL (or other logic based formalisms) Henson Graves Conrad Bock

  15. Why Integrate SysML With Logic Engineering has always been about building models of real world domains, analyzing models and making measurements refining and modifying the models Integrating a modeling language with logic-based system enables Standardization of model semantics Checking that model integration does not lead to inconsistency Automated reasoning tools to perform tasks which outstrip manual capability formal derivations for justification of engineering decisions

  16. How Does Integration With Logic Achieve Objectives Constructions in logical language are given (axiomatic and/or referential) semantics, e.g., Codify expected properties of language constructions such as subclass, instance, part,… Allow users as they model systems to not be dependent on subject matter experts to convey their meaning Use automated reasoning, based on formal semantics, for consistency checking as models are developed and merged Formal derivations (proofs) can provide justification for assumptions and decisions made on basis of models

  17. Some Different Approaches To Integration Learn some logic based language, say FOL, or OWL and use that recognizing that logic is primary Translate SysML into OWL and back in so far as is possible Switch back and forth Provide SysML with a formal logical foundation allow users to work within SysML and take advantage of reasoning tools

  18. A Stack of Semantic Standards for Ontologies Ontology Standards Common Vocabulary Modeling Language Standards Common Meaning INCREASING INTEROPERABILITY Metadata Standards Discovery Data Interchange Format Standards Exchange Resource Identification Standards Recovery Increasing use of standards To enable semantic interoperability

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