Uncertainty Reasoning for the Semantic Web I

Thisvolumecontainstheproceedingsofthe?rstthreeworkshopsonUncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2005, 2006, and 2007. In addition to revised and stronglyextendedversionsofselectedworkshoppapers,wehaveincludedinvited contributions from leading experts in the ?eld and closely related areas. With this, the present volume represents the ?rst comprehensive compilation of state-of-the-art research approaches to uncertainty reasoning in the context of the Semantic Web, capturing di?erent models of uncertainty and approaches to deductive as well as inductive reasoning with uncertain formal knowledge. TheWorldWide Web communityenvisionse?ortless interactionbetween- mansandcomputers,seamlessinteroperabilityandinformationexchangeamong Webapplications,andrapidandaccurateidenti?cationandinvocationofapp- priate Web services.As workwith semantics and servicesgrowsmoreambitious, there is increasing appreciation of the need for principled approaches to the f- mal representation of and reasoning under uncertainty.The term uncertainty is intended here to encompass a variety of forms of incomplete knowledge, - cluding incompleteness, inconclusiveness, vagueness, ambiguity, and others. The termuncertaintyreasoning ismeanttodenotethefullrangeofmethodsdesigned for representing and reasoning with knowledge when Boolean truth values are unknown, unknowable, or inapplicable. Commonly applied approachesto unc- tainty reasoning include probability theory, Dempster-Shafer theory, fuzzy logic and possibility theory, and numerous other methodologies. A few Web-relevant challenges which are addressed by reasoning under - certainty include: Uncertaintyofavailableinformation: MuchinformationontheWorldWide Web is uncertain. Examples include weather forecasts or gambling odds. Canonical methods for representing and integrating such information are necessaryforcommunicating it ina seamlessfashion.