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 I.Ontology Driven Simulation (ODS) –Definition & Motivation –Historical Perspective II.Web Based Resources for Modeling & Simulation –Domain Ontologies –Modeling Ontologies –Structured (e.g. databases) and Unstructured (e.g. papers) Sources III.Development of an ODS Prototype –ODS Architecture –Ontology Mapping Tool & Markup Language Generation –Executable Model Generation IV.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 A.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 1.Discovery Phase – Search and Browse Multiple Ontologies a.Relevant Domain Knowledge b.Applicable Modeling Techniques 2.Mapping Phase – a.Connect and transform classes, properties and instances in Domain Ontologies to those in Modeling Ontologies b.Generate any additional instances required in Modeling Ontology 3.Code Generation Phase a.Two-Stage: OWL  XML  Code Advantage: Many simulation work off of an XML dialect such as the Petri Net Markup Language (PNML) b.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. Map PMRO classes to DeMO Classes DeMO Represention of Model (OWL Instances) Generate Markup Language Instances Ontology Mapping Tool & Markup Language Generation <activity activityid="ClinicalExamination" activitytype="Facility" caption="Examination" "> <costdist distributiontype="Uniform" alpha="100.0" beta="300.0" stream="0" /> <servicedist distributiontype="Uniform" alpha="300.0" beta="200.0" stream="0" /> XPIML Representation of Model

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

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

12. Hospital Emergency Room Example Knowledge Extraction Model Construction

13. OWL Instance XPIML Instance JSIM Specification JSIM Execution

14. Biochemical Pathway ODS Knowledge Extraction Model Construction

15. Biochemical Pathway for Glycan Biosynthesis Michaelis-Menten Reaction Kinetics v 0 = Vmax[S] Km+[S] Hybrid Functional Petri Nets S1 E1 P1 E2 P2 R1R2 ES EB RA Glycan [S1] ES EB RA RNA Protien Enzyme [E1] ES EB RA Glycan [P1] ES EB RA [E2] ES EB RA Glycan [P2] RNA Protien Enzyme Substrate Enzyme Product Enzyme Product