A Rule-based Approach to Syntactic and Semantic Composition of BOMs

Creating simulation models via composition of predefined and reusable components is an efficient way of reducing costs and time associated with the simulation model development process. However, in order to successfully compose models one has to solve the issues of syntactic and semantic composability of components. HLA is the most widely used architecture for distributed simulations today. It provides a simulation environment and standards for specifying simulation parts and interactions between simulation parts. But it provides little support for semantic composability. The base object model (BOM) standard is an attempt to ease reusability and composition of simulation models. However, BOMs do not contain sufficient information for defining concepts and terms in order to avoid ambiguity, and provide no methods for matching conceptual models (state machines). In this paper, we present our approach for enhancement of the semantic contents of BOMs and propose a three-layer model for syntactic and semantic matching of BOMs. The semantic enhancement includes ontologies for entities, event and interactions in each component. We also present an OWLS description for each component including the state- machines. The three-layer model consists of syntactic matching, static semantic matching and dynamic semantic matching utilising a set of rules for reasoning about the compositions. We also describe our discovery and matching rules, which have been implemented in the Jess inference engine. In order to test our approach we have defined some simulation scenarios and implemented BOMs as building blocks for development of those scenarios, one of which has been presented in this paper. Our result shows that the three-layer model is promising and can improve and simplify composition of BOM-based components.