Uncertainty and the Semantic Web

Abstract

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).

Semantic Representation of Multimedia Content

“…Corresponding approaches have been developed in the context of ontology languages that extend the underlying mathematical frameworks so as to allow the formal handling of imperfect knowledge. Relevant proposals in the literature, include probabilistic DLs [16], probabilistic OWL [12], possibilistic DLs [40], as well as fuzzy DLs and fuzzy OWL [50][51][52].…”

“…Fuzzy interpretations can be extended to interpret complex f-SHOINconcepts and roles, with the aid of the fuzzy set theoretic operations defined and investigated in the area of fuzzy set theory [29]. The interested reader can refer to the wealth of fuzzy DL literature for the complete set of semantics [49,[51][52][53].…”

Multi Criteria Decision Making in Fuzzy Description Logics: A First Step

“…It consists in comparing elements (criteria) X i with X j (1 ≤ i, j ≤ k) and judge how much they contribute to the overall objective. The judgment consists in assigning a number w ij ∈ [1,9] Intermediate values can be used. The weight w i of element X i may be obtained asw…”

“…Fuzzy ontologies emerge as useful in several applications, such as (multimedia) information retrieval, image interpretation, ontology mapping, matchmaking and the Semantic Web [8]. So far, several fuzzy extensions of DLs can be found in the literature (see the survey in [8]) and some fuzzy DL reasoners have been implemented, such as fuzzyDL [3], DeLorean [2] or Fire [9].…”

Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies

“…In the Semantic Web domain the studies on uncertain reasoning are mostly focused on two formalisms: probability theory and fuzzy logic. Existing implementations of fuzzy description logic [9,10] are based on the notion of fuzzy set representing a vague concept. The uncertainty value in this context denotes a membership function µ F (x) which specifies the degree to which an object x belongs to a fuzzy class F .…”