Knowledge sharing and reuse has been considered an important issue for cost-effective use of knowledge-based systems, especially after the development and popularisation of objectbased technologies and Internet-based decentralised computing. Up until now, the majority of research tackling this issue has been founded on the assumption that there can be a common domain description - a shared ontology - which suits everyone with an interest in the knowledge. Unfortunately, getting an agreed ontology for a collection of systems can be a difficult problem and, even when this problem can be solved, it may not be enough for effective knowledge sharing, since the way we represent knowledge is intimately linked to the inferences we expect to perform with it. A nice example of this situation can be found in systems for reasoning under uncertainty, where even if we do have a shared ontology for the problem being solved we must still establish semantic links between the inferences performed within each system to actually have knowledge being shared and reused.
In the present work we study a significant instance of this problem. We introduce a simple yet effective logical system for interval-based probabilistic reasoning, then discuss the difficulties to have this system being able to consult bayesian belief networks to complete its own inferences, and how these difficulties can be remedied. Finally, some simple motivating examples are introduced to suggest "practical" applications for this knowledge sharing scenario.
This work is part of the DECaFf-KB (Distributed Environments for Cooperation among Formalisms for Knowledge-Based Systems) research initiative, partially funded by CAPES and the British Council and involving researchers from USP, UECE (Brazil), IIIA (Spain) and the University of Edinburgh.