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From Domain Ontologies to Modeling Ontologies to Executable Simulation Models

This paper explores the use of ontology-driven simulation (ODS) in model development, using domain ontologies to create simulation models. It discusses the historical perspective, web-based resources for modeling and simulation, development of an ODS prototype, and provides two examples of ODS in action.

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From Domain Ontologies to Modeling Ontologies to Executable Simulation Models

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  1. From Domain Ontologies to Modeling Ontologies to Executable Simulation Models Gregory A. Silver Osama M. Al-Haj Hassan John A. Miller University of Georgia 2007 Winter Simulation Conference

  2. Outline • Ontology Driven Simulation (ODS) • Definition & Motivation • Historical Perspective • Web Based Resources for Modeling & Simulation • Domain Ontologies • Modeling Ontologies • Structured (e.g. databases) and Unstructured (e.g. papers) Sources • Development of an ODS Prototype • ODS Architecture • Ontology Mapping Tool & Markup Language Generation • Executable Model Generation • ODS in Action: Two Examples • Hospital Emergency Department • Glycan Biosynthesis

  3. I. Ontology Driven Simulation • Definitions: • Domain Ontology – Knowledge in particular domains is captured through defining concepts, their relationships, and relevant constraints. • OWL (Web Ontology Language) is widely used for the Semantic Web. • Ontology Driven Simulation – Simulation model development assisted/driven by application domain knowledge stored in ontologies. • Motivation: Use the knowledge and data resident in domain ontologies to bootstrap the creation of simulation models.

  4. Historical Perspective • A Port Ontology for Automated Model Composition (Laing and Paredis 2003) • Discrete-event Modeling Ontology (DeMO) (Miller, et al. 2004) • Synthetic Environment Data Representation Ontology (sedOnto) (Bhatt, et al. 2005) • Evaluation of the C2IEDM as an Interoperability Enabling Ontology (Turnitsa and Tolk 2005) • Ontology Driven Framework for Simulation Modeling (Benjamin et al. 2005) • Process Interaction Modeling Ontology for Discrete Event Simulation (PIMODES) (Lacy 2006)

  5. II. Web Based Resources for Modeling & Simulation • Creation of simulation models requires gathering of substantial amounts of knowledge and data. • Sources of Information • Domain Ontologies – Domain Expertise • GlycO – Glycomics Ontology • EnzyO – Enzyme Ontology • PMRO – Problem-oriented Medical Records Ontology • Modeling Ontologies – Expertise in Modeling Techniques • Discrete-event Modeling Ontology (DeMO) • Online Databases • RK-Savio, BRENDA, KEGG • Text Mining • PubMed

  6. DeMO Top Level Classes

  7. III. Development of an ODS Prototype • Goals 1. Support the use of Multiple Modeling Technologies 2. Tools for extracting and mapping Domain Ontologies 3. Support code generation for several simulation engines B. The ODS Approach • Discovery Phase – Search and Browse Multiple Ontologies • Relevant Domain Knowledge • Applicable Modeling Techniques • Mapping Phase – • Connect and transform classes, properties and instances in Domain Ontologies to those in Modeling Ontologies • Generate any additional instances required in Modeling Ontology • Code Generation Phase • Two-Stage: OWL  XML  Code • Advantage: Many simulation work off of an XML dialect such as the Petri Net Markup Language (PNML) • One-Stage: OWL  Code • XML by itself is weak at expressing named relationships and constraints – so there is the potential for information loss.

  8. Ontology Driven Simulation Architecture

  9. Ontology Mapping Tool & Markup Language Generation DeMO Represention of Model (OWL Instances) Map PMRO classes to DeMO Classes Generate Markup Language Instances XPIML Representation of Model <activity activityid="ClinicalExamination" activitytype="Facility" caption="Examination" "> <location x="331" y="314" /> <costdist distributiontype="Uniform" alpha="100.0" beta="300.0" stream="0" /> <servicedist distributiontype="Uniform" alpha="300.0" beta="200.0" stream="0" /> </activity>

  10. Executable Model Generation XPIML Representation of Model <activity activityid="ClinicalExamination" activitytype="Facility" caption="Examination" "> <location x="331" y="314" /> <costdist distributiontype="Uniform" alpha="100.0" beta="300.0" stream="0" /> <servicedist distributiontype="Uniform" alpha="300.0" beta="200.0" stream="0" /> </activity> Executable Model Generator JSIM Execution

  11. IV. ODS in Action: Two Examples • Glycan Biosynthesis • GlycO, EnzyO  HFPN • Petri Nets • Hospital Emergency Room • PMRO  JSIM • Process Interaction

  12. Hospital Emergency Room Example Knowledge Extraction Model Construction

  13. OWLInstance XPIMLInstance JSIM Specification JSIM Execution

  14. Biochemical Pathway ODS Knowledge Extraction Model Construction

  15. Vmax[S] Michaelis-Menten Reaction Kinetics v0 = Km+[S] Hybrid Functional Petri Nets S1 P1 P2 R1 R2 Substrate Product Product E1 E2 Enzyme Enzyme [P2] [E1] [P1] [E2] [S1] ES EB RA ES EB RA ES EB RA ES EB RA ES EB RA Glycan RNA Protien Enzyme Glycan RNA Protien Enzyme Glycan Biochemical Pathway for Glycan Biosynthesis

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