T h e S e m a n t i c W e b imprecise knowledge. More precisely, some applications deal with random information and events, others deal with imprecise and fuzzy knowledge, and still others deal with missing or distorted information-resulting in uncertainty. For example, in applications involving sensor readings, such measurements usually come with degrees of evidence; in applications like multimedia processing, object recognition might come with degrees of truth. To deal with uncertainty in the Semantic Web and its applications, many researchers have proposed extending OWL and the Description Logic (DL) formalisms with special mathematical frameworks. Researchers have proposed probabilistic, 1 possibilistic, 2 and fuzzy extensions, 3-5 among others. Researchers have studied fuzzy extensions most extensively, providing impressive results on semantics, reasoning algorithms, and implementations. Building on these results, we've created a fuzzy extension to OWL called Fuzzy OWL. Fuzzy OWL can capture imprecise and vague knowledge-for example, we can say that Athens is hot to a degree 0.8 rather than saying that Athens is either hot or not. Moreover, our reasoning platform, Fuzzy Reasoning Engine (FiRE), lets Fuzzy OWL capture and reason about such knowledge (see www.image.ece.ntua.gr/~nsimou).
Non-Bayesian approaches include Fuzzy OWL REF , a fuzzy logic extension to OWL that aims to capture imprecise and vague knowledge.
Uncertainty and the Semantic Web
IEEE Intelligent Systems
IEEE Intelligent Systems
Computer Science