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The popularity and growth of the "Information SuperHighway" have dramatically increased the number of information sources available for use. Unfortunately, there are significant challenges to
overcome. One particular problem is context interchange, whereby each source of information and potential receiver of that information may operate
with a different context, leading to large-scale semantic heterogeneity. A context is the collection of implicit assumptions about the context definition (i.e., meaning) and
context characteristics (i.e., quality) of the information. This paper describes various forms of context challenges and examples of potential context mediation services, such as data semantics
acquisition, data quality attributes, and evolving semantics and quality, that can mitigate the problem. |
