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 object-based 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 this talk we analyse a significant instance of this problem. We introduce a simple yet effective logical system for interval-based reasoning, then discuss the difficulties to have this system being able to consult a bayesian belief network to compute its own inference. Conversely, we discuss the difficulties to have the belief network being able to consult the logical system.
See also the DECaFf-KB Project (Distributed Environment for Cooperation among Formalisms for Knowledge Bases) Web site.