There are a number of reasons to think that a workshop concerning the implementation of uncertainty reasoning in applications for the Semantic Web is timely at this stage of its evolution. The Semantic Web envisions effortless cooperation between humans and computers, seamless interoperability and information exchange among web applications, and rapid and accurate identification and invocation of appropriate Web services.
The ability of current-generation web technology to handle uncertainty is extremely limited, providing an inadequate foundation for knowledge interchange and application interoperability. Different applications have different ontologies, different semantics, and different knowledge and data stores. Legacy applications are usually only partially documented and may rely on tacit usage conventions that even proficient users do not fully understand or appreciate.
Further, the data that is exchanged in the context of the Semantic Web may itself be expressed in uncertainty. This suggests that recent work in the application of probability and decision theory to complex, open-world problems could be of vital importance to the success of the Semantic Web. Incorporating these new technologies into languages, protocols, and specifications for the Semantic Web is fundamental to bringing the Semantic Web vision to its full fruition. To further illustrate, consider a few web-relevant reasoning challenges that could be addressed or eased by utilizing reasoning under uncertainty.
Information extracted from large information networks such as the world wide web is typically incomplete. Knowing how and being able to utilize this partial information is very useful for identifying sources of service or information. For example, that an online service deals with greeting cards may be evidence that it also sells stationery. It is clear that search tools capable of utilizing probabilistic knowledge could increase search effectiveness.
Further, much information on the world-wide web is likely to be uncertain. Consider weather forecasts or gambling odds. A canonical method for representing and integrating such information is necessary for communicating such information in a seamless fashion.
Web information is also often incorrect or only partially correct raising issues related to trust or credibility. Uncertainty representation and reasoning helps to resolve tensions amongst information sources for purposes of approximating appropriately.
The Semantic Web vision implies numerous distinct but conceptually overlapping ontologies will co-exist and interoperate. It is likely that in such scenarios ontology mapping will benefit from the ability to represent degrees of overlapping, likelihood of membership in Class A of Ontology 1 given membership in Class B of Ontology 2.