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

OutlineI. 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

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.

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)

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

DeMO Top Level Classes

III. Development of an ODS PrototypeA. 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 Approach1. 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 Phasea. 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.

PMROOntology

GlycOOntology

ODS DesignTool Suite

DeMOOntology

DeMOInstances

Model MarkupGenerator

XPIM ePNMLBipartite Graph

LayoutAlgorithm

Graph LayoutAlgorithm

JSIMCode Generator

ARENACode Generator

ExecutableJSIM Model

ExecutableARENA Model

JSIM Petri NetEngine

JSIMExecution

Engine

ARENAExecution

Engine

ERPatientFlow

Modeling

GlycanBiosynthetic

Pathway Modeling

Ontology Editor, Browser,Viewer, and Alignment

Tools

SPARQL + XSLT

Event Oriented, StateOriented, Process Oriented,

Activity Oriented

AGLO

JavaProgram

JSIM Petri NetCode Generator

Executable PetriNet Model

Ontology Driven Simulation Architecture

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" "> <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>

XPIML Representation of Model

Executable Model Generation

<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

XPIML Representation of Model

JSIM Execution

IV. ODS in Action: Two Examples• Hospital Emergency Room

– PMRO JSIM– Process Interaction

• Glycan Biosynthesis– GlycO, EnzyO HFPN– Petri Nets

Hospital Emergency Room Example

PMROOntology

ODS Design ToolSuite

DeMOOntology

DeMOPIModel

Instances

Model MarkupGenerator

XPIMLInstances

JSIM Petri NetEngine

Knowledge Extraction

Model Construction

OWL Instance XPIML Instance

JSIM Specification

JSIM Execution

Biochemical Pathway ODS

GlycOOntology

EnzyOOntology

SystemsBiology

Ontology

ScientificLiterature

Biochemical- Pathways- ReactionKenitics

RK - SayioDatabase

ReactionLayers &

Constants

ODS Design ToolSuite

DeMOOntology

DeMOHFPNModel

Instances

Model MarkupGenerator

ePNMLInstances

JSIM Petri NetEngine

Knowledge Extraction

Model Construction

Biochemical Pathway for Glycan Biosynthesis

Michaelis-Menten Reaction Kinetics

v0 = Vmax[S]Km+[S]

Hybrid Functional Petri Nets

S1

E1

P1

E2

P2

R1 R2

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