Uncertainty reasoning for the "big data" semantic web

The Semantic Web introduces the concept of machine-oriented information, i.e. information that can be processed by machines or agents without human intervention. In order to achieve this, web information should be represented in a way that its semantics is understandable by agents. Defining semantics for web information is not an easy process, as the web information is not always clear-cut. For example, a web search for comfortable hotels introduces the vague concept comfortable. So, semantics are always related to some kind of vagueness. Moreover, the source of web information is always characterized by a notion of uncertainty, e.g Ninety percent of four star hotels have a swimming pool. Uncertainty and vagueness can be strongly related and this relation demands an extension of any representation scheme in order to capture imperfect concepts. Towards this notion we propose an ontology as well as a reasoning method suitable for imperfect data.