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Simulation and the Simulation and the Semantic WebSemantic Web

John A. MillerGregory Baramidze

Computer Science DepartmentUniversity of Georgia

Athens, GA 30602, U.S.A.

December, 2005

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Outline of the TalkOutline of the Talk

Ontology and the Semantic Web

Adding Semantics to Simulation

Purpose of the DeSO Ontology

DeSO Screenshots

Purpose of the DeMO Ontology

DeMO Screenshots

Applications and Benefits

Research Issues

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Semantic WebSemantic Web

Traditional WebTraditional Web HTMLHTML Keyword and page rank searchKeyword and page rank search

Semantic WebSemantic Web Several XML based languagesSeveral XML based languages Foundations and logicFoundations and logic Increased machine understandingIncreased machine understanding Query languages (e.g. SPARQL, SWOPS)Query languages (e.g. SPARQL, SWOPS)

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Semantic Web Architecture Semantic Web Architecture

LayerLayer Principle Principle LanguageLanguage NameName

Resource/DataResource/Data XML, DTD, XML, DTD, XSDXSD

eXtensible Markup eXtensible Markup LanguageLanguage

Meta-DataMeta-Data RDF/RDFSRDF/RDFS Resource Description Resource Description FrameworkFramework

OntologyOntology OWLOWL Ontology Web Ontology Web LanguageLanguage

LogicLogic SWRLSWRL Semantic Web Rule Semantic Web Rule LanguageLanguage

Proof/TrustProof/Trust Future workFuture work

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AcronymAcronym NameName ComplexityComplexity

OWL LiteOWL Lite Ontology Web Language – Minimal Ontology Web Language – Minimal (SHIF)(SHIF) EXP-TIMEEXP-TIME

OWL DLOWL DLOWL – Description LogicOWL – Description Logic

(SHOIN)(SHOIN)NEXP-TIMENEXP-TIME

OWL FullOWL Full OWL – Full Feature SetOWL – Full Feature Set Semi-decidableSemi-decidable

RDF(S)RDF(S) Resource Description Framework Resource Description Framework (Schema)(Schema) Semi-decidableSemi-decidable

KIFKIF Knowledge Interchange FormatKnowledge Interchange Format UndecidableUndecidable

UMLUML Unified Modeling Language (w/ OCL)Unified Modeling Language (w/ OCL) UndecidableUndecidable

Languages for Representing Ontologies

For Satisfiability and Subsumption

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Protégé Ontology EditorProtégé Ontology Editor

Supports OWL-Lite, DL, and FullSupports OWL-Lite, DL, and Full

Has an extension for SWRL rulesHas an extension for SWRL rules

Able to output human-readable syntaxAble to output human-readable syntax

Able to import multiple ontologiesAble to import multiple ontologies

Several visualization tools availableSeveral visualization tools available OntoViz, OWLViz, Jambalaya, TGVizOntoViz, OWLViz, Jambalaya, TGViz

Supports UML imports and exportsSupports UML imports and exports

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Ontologies for Scientific Domains

OntologyOntology NameName DomainDomainEngMathEngMath Engineering MathEngineering Math MathematicsMathematics

MonetMonet Mathematics on the WebMathematics on the Web MathematicsMathematics

EHEPEHEP Exp. High-Energy PhysicsExp. High-Energy Physics PhysicsPhysics

PhysicsOntoPhysicsOnto Ontology of PhysicsOntology of Physics PhysicsPhysics

OntoNovaOntoNova ONTOlogy-based NOVel q&A.ONTOlogy-based NOVel q&A. ChemistryChemistry

GOGO Gene OntologyGene Ontology GeneticsGenetics

MDEGMDEG Microarray Gene Expression Microarray Gene Expression DataData BiologyBiology

AstroGridAstroGrid Astronomy GridAstronomy Grid AstronomyAstronomy

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Increasing Realism and Increasing Realism and MeaningfulnessMeaningfulness

Abstract, low Abstract, low fidelity Modelfidelity Model

Greater Semantics

Greater Fidelity

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Purpose of DeSOPurpose of DeSO

A concise, but adequately precise ontologyA concise, but adequately precise ontology

The most fundamental concepts in modeling and The most fundamental concepts in modeling and simulationsimulation TimeTime: : t t T T SpaceSpace: : x(t) x(t) X X EntityEntity:: j j J J StateState: : s(t) = (xs(t) = (x11(t), …, x(t), …, xii(t), …)(t), …) EffectorEffector:: event or forceevent or force ModelModel:: integrates the above elementsintegrates the above elements

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DeSO in Protégé DeSO in Protégé

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DeSO ScreenshotsDeSO Screenshots

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Sample Abstract SyntaxSample Abstract Syntax

OWL ClassOWL Class Class(State partial owl:Thing)Class(State partial owl:Thing)

OWL Properties OWL Properties ObjectProperty(now Functional ObjectProperty(now Functional

domain(State) domain(State) range(Time)) range(Time))

ObjectProperty(currentValue ObjectProperty(currentValue domain(State) domain(State)

range(PhysicalEntity))range(PhysicalEntity))

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Sample Abstract SyntaxSample Abstract Syntax

SWRL RulesSWRL Rules existsIn(?m, ?st) populatedBy(?st, ?pe) effectedBy(?pe, ?f) ∧ ∧existsIn(?m, ?st) populatedBy(?st, ?pe) effectedBy(?pe, ?f) ∧ ∧

Force(?f) ∧ Force(?f) ∧

→ → isContinuousChange(?m, true)isContinuousChange(?m, true)

isContinuousChange(?m, false) isContinuousChange(?m, false)

→ → isDiscreteChange(?m, true)isDiscreteChange(?m, true)

existsIn(?m, ?st) populatedBy(?st, ?pe) effectedBy(?pe, ?ef) ∧ ∧existsIn(?m, ?st) populatedBy(?st, ?pe) effectedBy(?pe, ?ef) ∧ ∧ computes(?ef, ?f) StochasticFunction(?f) ∧ ∧ computes(?ef, ?f) StochasticFunction(?f) ∧ ∧

→ → isStochastic(?m, true)isStochastic(?m, true)

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Purpose of DeMOPurpose of DeMO

DeSO is a higher-level ontology with a DeSO is a higher-level ontology with a broad scopebroad scopeDeMO is more narrowly focused DeMO is more narrowly focused Discrete-event modelingDiscrete-event modeling

DeMO is more fully developedDeMO is more fully developed ModelConceptModelConcept ModelComponentModelComponent ModelMechanismModelMechanism DeModelDeModel

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DeMO ModelConceptDeMO ModelConcept

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DeMO ModelComponentDeMO ModelComponent

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DeMO ModelMechanismDeMO ModelMechanism

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DeMO DeModelDeMO DeModel

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Applications and BenefitsApplications and Benefits

• Browsing. One could look at the concepts in the ontology and navigate to related concepts using Protégé or visualization tools.

• Querying. Query languages under development (e.g., RQL, SPARQL, DQL, OWL-QL, SWOPS).

• Service Discovery. One could look for a Web service to perform a certain modeling task (e.g. semantic web service discovery using WSDL-S).

• Components. DeSO/DeMO can serve as Web-based infrastructure for storing and retrieving executable simulation model components. These components can facilitate reuse.

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•Research Support. Papers in the field of modeling and simulation may be linked into the ontology to help researchers find more relevant research papers more rapidly. These links can be added manually or through automatic extraction/classifications tools such as those provided by Semagix (www.semagix.com).

• Mark-up Language Anchor. Domain-specific XML-based mark-up languages allow interfaces to software or descriptions of software to be presented in platform and machine-independent ways. The tags used in the markup language should ideally be anchored in a domain ontology. In the simulation community such work has begun (e.g., XML for rube (Fishwick, 2002b)).

This enhances the interoperability of simulation models.

• Facilitate Collaboration. Shared conceptual framework provides opportunities for increased collaboration, including interoperability of simulation tools, model reuse and data sharing.

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Research Issues with DeSO/DeMOResearch Issues with DeSO/DeMO

Depth vs. breadth of ontologyDepth vs. breadth of ontology

Design choices for the ontologyDesign choices for the ontology

Issues of ambiguityIssues of ambiguity (multiple ways of defining (multiple ways of defining concepts and how to deal with them)concepts and how to deal with them)

Mappings between various modeling Mappings between various modeling formalismsformalisms

Relating different ontologies Relating different ontologies (e.g., DeSO (e.g., DeSO imports SUMO)imports SUMO)

Combining instance-based and conceptual Combining instance-based and conceptual knowledgeknowledge ((linking DeMO with simulation engineslinking DeMO with simulation engines))

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Thank You.Thank You.

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What is an Ontology?What is an Ontology?Traditional:Traditional: a branch of metaphysics concerned with a branch of metaphysics concerned with

the nature and relations of beingthe nature and relations of being . .

Merriam-WebsterMerriam-Webster

Information Technology: “An explicit formal Information Technology: “An explicit formal specification of how to represent the specification of how to represent the objects, concepts and other entities that objects, concepts and other entities that are assumed to exist in some area of are assumed to exist in some area of interest and the relationships that hold interest and the relationships that hold among them.”among them.”or more concisely:“An ontology is a formal, explicit specification of a shared conceptualization.”

Gruber, T. R

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Using the Semantic Web in SimulationUsing the Semantic Web in Simulation

Study the potential use, benefits and the developmental requirements of Web-accessible ontologies for discrete-event simulation and modeling. As a case study we’ve developed a prototype OWL-based ontology :

Discrete-event Modeling Ontology

(DeMODeMO)

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Upper and mid-level ontologiesUpper and mid-level ontologies

Modeling and Simulation Ontology should Modeling and Simulation Ontology should eventually be build from eventually be build from upper ontologiesupper ontologies defining foundational concepts. defining foundational concepts.

Examples:Examples: Suggested Upper Merged Ontology (Suggested Upper Merged Ontology (SUMOSUMO)) Standard Upper Ontology (Standard Upper Ontology (SUOSUO)) OpenMathOpenMath MathMLMathML

MONET (Mathematics On the NET)

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Existing taxonomies in simulation and Existing taxonomies in simulation and modeling modeling

Classification may be based on various characteristicsStatic vs. Dynamic

Discrete vs. ContinuousDeterministic vs. Stochastic

Time-varying vs. Time-invariantDescriptive vs. Prescriptive

Time-driven vs. Event-driven Analytic vs. Numeric

Classification may be based on existing taxonomies

Simulation World Views: Event-scheduling, Activity-scanning, Process-interaction,

State-based

Programming Paradigms:Declarative, Functional, Constraint

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DeMO – Discrete-event Modeling OntologyDeMO – Discrete-event Modeling Ontology

The main goal was to investigate the principles of construction of an ontology for discrete-event modeling and to flush out the problems and promises of this approach, as well as directions of future research.

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DeMO Design ApproachDeMO Design ApproachTo build a discrete-event modeling ontology essentially means to capture and conceptualize as much knowledge about the DE modeling domain as possible and/or plausible.

We start with the more general concepts and move down the hierarchy, while building necessary interconnections as we go.

DeMO has four main abstract classes representing the main conceptual elements of this knowledge domain:

DeModel, DeModel, ModelConcepts, ModelConcepts,

ModelComponents, ModelComponents, ModelMechanismsModelMechanisms

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Rationale behind DeMO design

Any Any DeModelDeModel is built from is built from model componentsmodel components and is “put in motion” by and is “put in motion” by model mechanismsmodel mechanisms, ,

which themselves are built upon the most which themselves are built upon the most fundamental fundamental model conceptsmodel concepts..

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MODEL CONCEPTSMODEL CONCEPTS

MODEL MECHANISMSMODEL MECHANISMS

The most basic, fundamental terms upon which the ontology is built. Some of the concepts may not be formally defined.

In this respect similar to geometric terms as point, line, etc.

Specify the “rules of engagement” adopted by different models. In essence “explain how to run the model”.

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Protégé - OWLProtégé - OWLTo build DeMO we used Protégé -- an open-source ontology editor and a knowledge-base editor. (http://protege.stanford.edu/)

Protégé OWL plug-in allows one to easily build ontologies that are backed by OWL code.

Classes - represent concepts from the knowledge domain (e.g., the class Person)

Individuals - specific instances of classes (e.g., the tall Person that lives in 12 Oak st.)

Properties - determine the values allowed to each individual (e.g., the specific Person has height 190 cm)

OWL ontologies roughly contain three types of resources:

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CLASSES

Class description

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BACKBONE TAXONOMYIN PROTEGE

A backbone taxonomy is our mental starting point for building an ontology.

It is defined based on

i World-views of the models

ii Subsumption relationships

DeModel class is the root of the backbone taxonomy

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MODEL COMPONENTS

This class describes the elements that are used as the building blocks of DeModel classes.

Generally correspond to the elements in n-tuples traditionally used in the literature to formally define the models.

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Research Issues with DeMOResearch Issues with DeMO

Depth vs. breadth of ontologyDepth vs. breadth of ontology

Design choices for the ontologyDesign choices for the ontology

Issues of ambiguityIssues of ambiguity (multiple ways of defining (multiple ways of defining concepts and how to deal with them)concepts and how to deal with them)

Mappings between various modeling Mappings between various modeling formalismsformalisms

Relating different ontologies Relating different ontologies (e.g., a future Math (e.g., a future Math ontology, or an ontology for Graph Topology)ontology, or an ontology for Graph Topology)

Combining instance-based and conceptual Combining instance-based and conceptual knowledgeknowledge ((linking DeMO with simulation engineslinking DeMO with simulation engines))

Terminal pointsTerminal points (where do we stop the ontology)(where do we stop the ontology)

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Potential BenefitsPotential Benefits• Browsing. One could look at the concepts in the ontology and navigate to related concepts.

• Querying. Query languages under development (e.g., RQL, DQL, OWL-QL) and more. Next layer, the logic layer (e.g., SWRL and FORUM). Given such query capabilities, queries on DeMO would be very useful.

• Service Discovery. One could look for a Web service to perform a certain modeling task (Verma et al.,2003), (Chandrasekaran et al., 2002).

• Components. DeMO can serve as Web-based infrastructure for storing and retrieving executable simulation model components. These components can facilitate reuse. (e.g. Code implementations of specific probability density functions can be attached directly to the ProbabilisticTransitionFunction link, and they are made available to those searching for them.)

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• Hypothesis Testing. The LSDIS Lab is currently carrying out funded research to allow hypothesis testing to be performed using the Semantic Web (Sheth et al., 2003). In the future, this capability could be used to pose challenging questions such as which adaptive routing algorithm will work best on the evolving Internet.

• Research Support. Papers in the field of modeling and simulation may be linked into the ontology to help researchers find more relevant research papers more rapidly. These links can be added manually or through automatic extraction/classifications tools such as those provided by Semagix (www.semagix.com).

• Mark-up Language Anchor. Domain-specific XML-based mark-up languages allow interfaces to software or descriptions of software to be presented in platform and machine-independent ways. The tags used in the markup language should ideally be anchored in a domain ontology. In the simulation community such work has begun (e.g., XML for rube (Fishwick, 2002b)).

This enhances the interoperability of simulation models. • Facilitate Collaboration. Shared conceptual framework provides opportunities for increased collaboration, including interoperability of simulation tools, model reuse and data sharing.

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AppendixAppendix

Screen shots and definitionsScreen shots and definitions

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Instances of classes (individuals)

TransitionTriggering is a ModelMechanism and has two instances:_Multiple_Event_Triggering and _Single_Event_Triggering

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Example of OWL code

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What is an Ontology?What is an Ontology?Traditional:Traditional: a branch of metaphysics concerned with a branch of metaphysics concerned with

the nature and relations of beingthe nature and relations of being . .

Merriam-WebsterMerriam-Webster

Information Technology: “An explicit formal Information Technology: “An explicit formal specification of how to represent the specification of how to represent the objects, concepts and other entities that objects, concepts and other entities that are assumed to exist in some area of are assumed to exist in some area of interest and the relationships that hold interest and the relationships that hold among them.”among them.”or more concisely:“An ontology is a formal, explicit specification of a shared conceptualization.”

Gruber, T. R

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Knowledge Representation and Ontologies Knowledge Representation and Ontologies

Catalog/ID

GeneralLogical

constraints

Terms/glossary

Thesauri“narrower

term”relation

Formalis-a

Frames(properties)

Informalis-a

Formalinstance

Value Restriction

Disjointness, Inverse,part of…

Ontology Dimensions After McGuinness and FininOntology Dimensions After McGuinness and Finin

SimpleTaxonomies

Expressive

Ontologies

Wordnet

CYCRDF DAML

OO

DB Schema RDFS

IEEE SUOOWL

UMLS

GO

KEGG TAMBIS

EcoCyc

BioPAX

GlycO

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Many of the ModelComponents characterizing different first-level formalisms are either identical in meaning or translatable into each other. These relationships may be captured using description logic tools provided by OWL, thus placing stronger semantic connections between the model components.

e.g.All first-level formalisms use TimeSet as a formal component. ClockFunction is another example, although it may have slightly different meaning in different first-level formalisms.

MODEL COMPONENTS

4848 StochasticClockFunction class and its properties

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• If the domain ontology is too broad it may become too complex and disjointed. Ambiguities may be quite difficult to resolve.

• On the other hand, if it is too narrow, it is of limited use.

Breadth vs Width of the Breadth vs Width of the Ontology.Ontology.

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Handling of Multiple Handling of Multiple Taxonomies.Taxonomies.

What is the best way to embed multiple What is the best way to embed multiple taxonomies in the ontology? Should a taxonomies in the ontology? Should a principal taxonomy be picked as the principal taxonomy be picked as the backbone (subsumption of modeling backbone (subsumption of modeling techniques was chosen in DeMO). The techniques was chosen in DeMO). The other taxonomies then became other taxonomies then became secondary (e.g., determinacy, secondary (e.g., determinacy, application area, etc.).application area, etc.).

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Further categorizationFurther categorization

Knowledge subdomains such as Knowledge subdomains such as ModelMechanisms, for example, require ModelMechanisms, for example, require further formal categorization further formal categorization (at present (at present English descriptions are used for ModelMechanisms).English descriptions are used for ModelMechanisms).

Deeper relationships between the Deeper relationships between the concepts (such as mappings between concepts (such as mappings between different modeling formalisms, for different modeling formalisms, for example) need to be developed.example) need to be developed.

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Design choices for the ontologyDesign choices for the ontology

Do we have to have a unified theory Do we have to have a unified theory where top level formalisms are some where top level formalisms are some special cases of one general model?special cases of one general model?

Can we create different ontologies and Can we create different ontologies and merge/link them together without merge/link them together without developing a unifying formalism?developing a unifying formalism?