Publishing Uncertainty on the Semantic Web: Blurring the LOD Bubbles

Description

.The open nature of the Web exposes it to the many imper-fections of our world. As a result, before we can use knowledge obtainedfrom the Web, we need to represent that fuzzy, vague, ambiguous and un-certain information. Current standards of the Semantic Web and LinkedData do not support such a representation in a formal way and indepen-dently of any theory. We present a new vocabulary and a framework tocapture and handle uncertainty in the Semantic Web. First, we definea vocabulary for uncertainty and explain how it allows the publishingof uncertainty information relying on different theories. In addition, weintroduce an extension to represent and exchange calculations involvedin the evaluation of uncertainty. Then we show how this model and itsoperational definitions support querying a data source containing differ-ent levels of uncertainty metadata. Finally, we discuss the perspectiveswith a view on supporting reasoning over uncertain linked

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