A knowledge-driven framework for simulation application integration

Abstract

This paper describes the motivations, methods, and solution concepts of a knowledge-driven framework for simulation application integration. First, the solution ideas are motivated by providing a characterization of the challenges associated with simulation integration at the semantic level. Next, opportunities provided by recent advances in knowledge representation and ontology development methods are outlined. The important role of ontologies in simulation integration is then briefly described. Next, a method for knowledge-driven simulation application integration is described in detail. Illustrative application examples are then outlined in order to provide a flavour for the practical value for the research. Finally, we outline the potential benefits of the research.

Keywords:

• knowledge-based simulation
• simulation application integration
• ontology management
• semantic interoperability
• simulation composability

Notes

1 Our experience in building simulation-based applications indicates that conceptual models are usually ‘represented’ in a non-formal manner. Often, the conceptual model exists only in the mind of the analyst/modeller. The informal representation of the model is expressed in the formal of pictures/diagrams or handwritten notes. In order to support automated processing of the information contained within a conceptual model, there is a need to perform a ‘formal’ encoding of the model information. We have used the IDEF3 process description language and the IDEF5 ontology description language to encode conceptual simulation models (www.idef.com) (Note: both IDEF3 and IDEF5 are ‘first-order’ languages in terms of expressive power). In general, a first-order language is expressively adequate to represent the information in a (discrete event) simulation conceptual model. The level of detail required in the conceptual model is determined by the modelling objectives; in general, the level of detail required of the conceptual model is proportional to the level of detail required in the executable simulation model.

2 KSAI is a more detailed method compared with the distributed simulation engineering and execution process (DSEEP)—a standard prescribed by the Simulation Interoperability Standards Organization (http://www.sisostds.org/StandardsActivities/DevelopmentGroups/DSEEPPDGDistributedSimulationEngineeringand.aspx). DSEEP describes a high-level seven-step process for the lifecycle development of distributed simulations. The KSAI method complements and supplements the DSEEP in several ways. First, the role and use of ontologies in simulation development is not explicitly described in DSEEP. The description of how ontologies may be used to help simulation application development is both consistent with and complementary to DSEEP. KSAI also describes the conceptual simulation modelling process in greater detail and outlines the role of ontologies in this important activity. Lastly, the KSAI ontology comparison and alignment methods will supplement DSEEP adopters by providing useful guidelines for distributed simulation application integration at the semantic level.

3 The ontology comparison methods described in this section are related to the ‘T-Box–T-Box and A-Box–A-Box’ (schema and ontology) alignment techniques described in the literature. The main difference is the scope: this paper describes a more comprehensive and general approach to ontology comparison that may be used for a broad range of applications. The ontology and schema alignment methods are narrower in application scope.