Ontology-Based M&S Dictionaries:

An Example for V&V

update and ã by Tuncer Ören 2006-09-09

http://www.site.uottawa.ca/~oren/

Ontologies are used to represent relationships of concepts. See for example Fishwick and Miller for ontologies for M&S (2004). There are different ways relationships can be represented. For example in Visual Thesaurus, a graphic relationship is augmented with definitions [VT]. Ontology-based dictionaries can be used to represent the conceptual relationships of M&S terms.

Several interwoven concepts and terms exist related with quality of modeling and simulation, software, user/system interfaces, as well as V&V techniques.  Ways to classify and present the relevant information as an ontology-based dictionary would be useful for practitioners as well as for students.

As an example, we elaborate on a small number of M&S V&V terms given in Table 1 [Ören et al., 1985]. For a complete list of M&S quality assurance terms, please see Ören [1984]. Table 2 is a conventional dictionary (arranged alphabetically by the terms) for the M&S V&V terms listed in Table 1.

In quality assurance studies, most of the terms represent evaluation of an element with respect to a criterion.  Hence, in an ontology-based dictionary, as in Tables 3 and 4, col. 1 shows the element which is evaluated while col. 3 shows the criterion with respect to which the evaluation is done. Col. 2 can be used to refer to a subset of an element represented in col. 1. In reading a definition of a term in an ontology-based dictionary, one can also see –in a systematic way– the relationship (and the nature of relationship) of a term with other terms.  In an ontology-based dictionary, the relationships can be very varied.

For cases where an evaluation criterion cannot be identified, col. 3 can be left blank.

A regular dictionary (arranged alphabetically by the terms) would consist of only columns 4 and 5 to display the terms and the corresponding definitions and possibly pointers to relevant additional concepts.

A 6^th column can –be added to a systematic dictionary to indicate pointers to applicable technique(s). Similarly a 7^th column can be added for references.

An ontology-based dictionary can be on a CD or can be made available on a Web site where all the searchable terms can be arranged in another alphabetical list. For example, the list of the searchable terms covered in Tables 3 and 4 are shown in Table 1.  (Since Tables 3 and 4 are provided as examples –they don’t include all the terms; hence, the terms given in Table 1 are a small subset of the relevant terms.)  Terms consisting of more than one word, e.g., “experimentation error” can be listed more than once, e.g., “experimentation error” and “error, experimentation,” to be able to search them grouped in different ways; in this case, under experimentation and error.

References:

Fishwick, P.A., Miller, J.A.  (2004). Ontologies for Modeling and Simulation: Issues and Approaches. Proceedings of the Winter Simulation Conference, 259-264.

Ören, T.I. (1984). Quality Assurance in Modelling and Simulation: A Taxonomy. In: Simulation and Model-Based Methodologies: An Integrative View, T.I. Ören, B.P. Zeigler, M.S. Elzas (eds.). Springer-Verlag, Heidelberg, Germany, pp. 477-517.

Ören, T.I., Elzas, M.S., Sheng, G. (1985). Model Reliability and Software Quality Assurance in Simulation of Nuclear Fuel Waste Management Systems. In: Waste Management '85, Vol. 1, R.G. Post (ed.), pp. 381-396. 

VT - Visual Thesaurus. http://www.visualthesaurus.com/

Publications of Dr. Tuncer Ören

- on M&S Quality Assurance (QA) are listed at:

  http://www.site.uottawa.ca/~oren/pubsList/QA.ht

- on M&S taxonomies are listed at:    

  http://www.site.uottawa.ca/~oren/pubsList/taxonomies.htm

Table 1.   List of Searchable Terms for the Example on

an Ontology-based Dictionary for M&S V&V

Table 2. A Conventional Dictionary for the M&S V&V Terms Listed in Table 1

Table 3.  Part of an Ontology-based Dictionary

1	2	3	4	5
Evaluation of		with respect to	Term	Definition/Comment (and relevant concepts)
Conceptual model		Goal	Model qualification	Evaluation of a conceptual model with respect to the goal of the study. (model relevance, model adequacy)
		Real system	Model realism	Evaluation of a conceptual model with respect to the real system.

Data (behavior):	Real system data (Real system behavior)	Goal	Data relevance	Evaluation of data collected from the real system with respect to the goal of the study. (data accuracy)
		Real system	Instrumenta-tion error	Evaluation of data collected from the real system with respect to real-system as the source of data, taking into account error tolerance.
		Experimental conditions	Experimenta-tion error	Evaluation of the procedure of collecting data from real system with respect to experimental conditions.
	Simulated data (Model behavior)	Real system behavior	Behavioral model validity (as a black box)	Comparison of model behavior and real system behavior observed or generated under same/similar conditions.
		Behavior of same model under different scenarios	Sensitivity of model behavior	Comparison of model behavior under different scenarios where all conditions are kept same, except for the parameter and/or initial condition for which sensitivity is being assessed.
		Behavior of another model under same scenario	Behavioral comparison of models	Comparison of behavior of a model with the behavior of another model generated under same conditions

 

Table 4 Part of an Ontology-based Dictionary

1	2	3	4	5
Evaluation of		with respect to	Term	Definition/Comment (and relevant concepts)

Simulation Program:	Simulation model (Computerized model)	Real system	Structural model verification	Evaluation of the structure of a model with respect to perceived structure of real system
		Conceptual model	Model verification	Evaluation of a simulation model with respect to a conceptual model
		Another model	Structural model comparison	Evaluation of the structure of a simulation model with respect to the structure of another simulation model.
		Modelling formalism	Formal check	Evaluation of the simulation model with respect to the modeling formalism used. (consistency checks, completeness checks for built-in quality assurance)
		Software engineering requirements	Software quality assurance of simulation  model	Evaluation of a simulation model with respect to software quality assurance requirements.
	Computerized experiments	Experimental conditions	Verification of experimental conditions	Evaluation of a computerization of experiments with respect to experimental conditions
		Software engineering requirements	Software quality assurance of  computerized experiments	Evaluation of computerization of the experiments with respect to software quality assurance requirements.
	Run-time simulation library	Software engineering requirements	Software quality assurance of  run-time simulation library	Evaluation of the simulation run-time library with respect to software quality assurance requirements.