ONTOLOGIES FOR MODELING AND SIMULATION: ISSUES AND APPROACHES Part II. Paul A. Fishwick CISE University of Florida Gainesville, FL 32611, U.S.A. John A. Miller Computer Science Department University of Georgia Athens, GA 30602, U.S.A. December, 2004. What is it we are trying to do?.
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Paul A. FishwickCISEUniversity of FloridaGainesville, FL 32611, U.S.A.
John A. MillerComputer Science DepartmentUniversity of GeorgiaAthens, GA 30602, U.S.A.
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
MONET (Mathematics On the NET)
Classification may be based on various characteristics
Static vs. Dynamic
Discrete vs. Continuous
Deterministic vs. Stochastic
Time-varying vs. Time-invariant
Descriptive 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,
Declarative, Functional, Constraint
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.
To 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:
Any DeModel is built from model components and is “put in motion” by model mechanisms, which themselves are built upon the most fundamental model concepts.
Specify the “rules of engagement” adopted by different models. In essence “explain how to run the model”.
To 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.
OWL ontologies roughly contain three types of resources:
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)
A backbone taxonomy is our mental starting point for building an ontology.
It is defined based on
i World-views of the models
DeModel class is the root of the backbone taxonomy
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.
Screen shots and definitions
TransitionTriggering is a ModelMechanism and has two instances:
_Multiple_Event_Triggering and _Single_Event_Triggering
Traditional:a branch of metaphysics concerned with the nature and relations of being .
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
EcoCycKnowledge Representation and Ontologies
Disjointness, Inverse,part of…
Ontology Dimensions After McGuinness and Finin
StochasticClockFunction class and its properties