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1
ONTOLOGIES FOR MODELING AND
SIMULATION:ISSUES AND APPROACHES
Part II
John A. MillerComputer Science Department
University of GeorgiaAthens, GA 30602, U.S.A.
Paul A. FishwickCISE
University of FloridaGainesville, FL 32611, U.S.A.
December, 2004
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What is it we are trying to do?
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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Semantic Web Architecture Semantic Web Architecture
LayerPrinciple Language
Name
Resource/Data XMLeXtensible Markup
Language
Meta-Data RDFResource Description
Framework
Ontology OWLOntology Web
Language
Logic SWRLSemantic Web Rule
Language
Proof/Trust Future work
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Acronym Name Complexity
OWL LiteOntology Web Language – Minimal (SHIF)
EXP-TIME
OWL DLOWL – Description Logic
(SHOIN)NEXP-TIME
OWL Full OWL – Full Feature Set Semi-decidable
RDF(S)Resource Description Framework (Schema)
Semi-decidable
KIF Knowledge Interchange Format Undecidable
UML Unified Modeling Language (w/ OCL) ?
Languages for Representing Ontologies
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Upper and mid-level ontologies
• Modeling and Simulation Ontology should eventually be build from upper ontologies defining foundational concepts.
• Examples:– Suggested Upper Merged Ontology (SUMO)– Standard Upper Ontology (SUO)– OpenMath– MathML
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 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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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 DeMO
• Depth vs. breadth of ontology• Design choices for the ontology• Issues of ambiguity (multiple ways of defining concepts
and how to deal with them)
• Mappings between various modeling formalisms• Relating different ontologies (e.g., a future Math
ontology, or an ontology for Graph Topology)
• Combining instance-based and conceptual knowledge (linking DeMO with simulation engines)
• Terminal points (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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Instances of classes (individuals)
TransitionTriggering is a ModelMechanism and has two instances:_Multiple_Event_Triggering and _Single_Event_Triggering
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What is an Ontology?Traditional: a branch of metaphysics concerned with the
nature and relations of being .
Merriam-Webster
Information Technology: “An explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold 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
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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Ontologies for Scientific Domains
Ontology Name Domain
EngMath Engineering Math Mathematics
EHEP Exp. High-Energy Physics Physics
OntoNova ONTOlogy-based NOVel q&A. Chemistry
GO Gene Ontology Genetics
MDEGMicroarray Gene Expression
DataBiology
AstroGrid Astronomy Grid Astronomy
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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
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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 Ontology.
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Handling of Multiple Taxonomies.
• What is the best way to embed multiple taxonomies in the ontology? Should a principal taxonomy be picked as the backbone (subsumption of modeling techniques was chosen in DeMO). The other taxonomies then became secondary (e.g., determinacy, application area, etc.).
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Further categorization
• Knowledge subdomains such as ModelMechanisms, for example, require further formal categorization (at present English descriptions are used for ModelMechanisms).
• Deeper relationships between the concepts (such as mappings between different modeling formalisms, for example) need to be developed.