Semantic heterogeneity in heterogeneous autonomous databases poses problems in instance matches, units conversion (value interpretation), contextual and structural mismatches, etc. In this work we examine some of the research issues in semantic heterogeneity and propose a novel architecture for resolving such problems. The approach involves the use of Artificial Intelligence tools and techniques to construct "domain models, " that is data and knowledge representations of the constituent databases and an overall domain model of the semantic interactions among the databases. These domain models are represented as Knowledge Sources (KSs) in a blackboard architecture. This architecture lends itself to an opponunistic approach to query processing and goal-directed problem solving. We introduce the notion of Data/Knowledge packets as a means of supporting semantic heterogeneity, and show how an active and intelligent global thesaurus can be used to reformulate queries based on knowledge associated with terms and their usage in local databases.
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.