Knowledge acquisition/discovery, ontology management, knowledge representation and knowledge sharing are key
issues in ontology research. This paper focuses on the issue of applying ontology management and analysis to facilitate
the reusability of simulation components based on ontology comparisons. Novel ontology comparison methods for
component-based simulation composition are described, such as the four independent approaches for ontology similarity
analysis: terminology-based, feature-based, semantic, and topological. A data -fusion-based approach is used to
integrate the information from these three techniques into a single similarity score. The assignment of such similarity
indices and the use of data fusion to obtain an ontology comparison similarity score are demonstrated using a simple example.
This paper identifies and addresses the issues associated with modeling and simulation and multiple levels of abstraction,
or multi-resolution modeling (MRM). An extensive literature review is conducted to encompass all schools of thought in
the area into this research. We begin by outlining the need for MRM and describe the problems encountered when two or
more models developed at different resolutions are to be integrated into a single application. These problems can
manifest themselves in different ways in the model, depending on the specific phenomenon being modeled. A distinction
is made in identifying these manifestations based on whether the underlying model is a process model such as an IDEF3
model, or an executable simulation model. Heuristic approaches have developed to assist with different aspects of model
composability efforts. Finally, a rule-based approach has been developed to identify any such problems, or abstraction
mismatches, that may occur if the two models are integrated into a single application. A conceptual description of these
rules and their motivation is provided.
This paper describes the motivations, methods, and solution concepts of a novel Framework for Adaptive Modeling and Ontology-driven Simulation (FAMOS). FAMOS uses a hybrid approach that combines ontology and process analysis methods with ontology-driven translation generation techniques to facilitate (1) robust simulation composability analysis and (2) semantic modeling and simulation interoperability. FAMOS provides enabling technology that addresses the technical challenges in three areas: (1) Modeling and Simulation Composability, (2) Semantic Interoperability and Information Sharing, and (3) Model Composition at Multiple Levels of Abstraction. The paper will (1) outline the technical challenges targeted by our research, (2) describe the FAMOS Ontology-driven Simulation Application Integration (OSAI) Method, and (3) introduce the FAMOS solution architecture that provides automated support for the OSAI method.