Semantic Interoperability through Context Interchange: Representing and Reasoning about Data Conflicts in Heterogeneous and Autonomous Systems

This paper describes a strategy for the construction of large-scale heterogeneous sys- tems which combines the benefits of both tight-coupling systems (insulating users from the complexity of semantic heterogeneity thus promoting greater usability) and loose-coupling systems (allowing changes in individual components to be contained locally thus assuring the long-term viability of the system as a whole). We define a new data model (COIN) based on a deductive and object-oriented language with which knowledge of data semantics is rep- resented in the form of enriched schemas, contexts and underlying domain models. Query mediation in this framework is driven by a top-down evaluation strategy which yields a query plan describing what sources are used and what conversions must be applied in mediating semantic conflicts. The COIN data model induces a novel dichotomy between schemas and contexts thus permitting knowledge of data semantics to be shared and reused across dif- ferent systems in similar environments. This in turn provides various options for querying disparate sources, with each offering a different level of transparency depending on the needs of the users. The richness of the model offers receivers more flexibility, both in defining what disparities should count as conflicts, and how conflicts should be resolved.