Reasoning within Fuzzy Description Logics

By Umberto Straccia. Journal of Artificial Intelligence Research, 2011

563 citations recorded by the source corpus.

Description Logics (DLs) are suitable, well-known, logics for managing structured knowledge. They allow reasoning about individuals and well defined concepts, i.e. set of individuals with common properties. The experience in using DLs in applications has shown that in many cases we would like to extend their capabilities. In particular, their use in the context of Multimedia Information Retrieval (MIR) leads to the convincement that such DLs should allow the treatment of the inherent imprecision in multimedia object content representation and retrieval. In this paper we will present a fuzzy extension of ALC, combining Zadeh's fuzzy logic with a classical DL. In particular, concepts becomes fuzzy and, thus, reasoning about imprecise concepts is supported. We will define its syntax, its semantics, describe its properties and present a constraint propagation calculus for reasoning in it.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/86b68677148c131e7224835eeda30bef0cf7cc5e

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Citations in the corpus

Managing uncertainty and vagueness in description logics for the Semantic Web (2008, methodology): “Every probabilistic knowledge base consists of (i) a PTBox, which is a classical (description logic) knowledge base along with Please cite this article in press as: T. Lukasiewicz, U. Straccia, Managing uncertainty and vagueness in description logics for the Semantic Web, probabilistic terminologica”
Applying Fuzzy DLs in the Extraction of Image Semantics (2009, methodology): “The sequence of works by Straccia [47, 48, 53] and Stoilos et al.”
Fuzzy Ontology Representation using OWL 2 (2010, unknown): “A23) irr(S) 8x2 I;SI(x;x) = 0 (A24) sym(R) 8x;y2 I;RI(x;y) = RI(y;x) (A25) asy(S) 8x;y2 I, if SI(x;y) >0 then SI(y;x) = 0 7 3.3. Reasoning tasks There are several reasoning tasks in fuzzy SROIQ(D) [23, 26]. Fuzzy KB satisability. A fuzzy interpretation Isatises (is a model of) a fuzzy KB Ki it sa”
On scene interpretation with description logics (2006, methodology): “There exist several approaches for merging DLs and probabilities, motivated partly by interest to fuzzify crisp concept membership [53], partly by the goal to replace Bayesian Network nodes by structured expressions [54–56].”
fuzzyDL: An expressive fuzzy description logic reasoner (2008, unknown): “We next present the fuzzy DL which is currently behind the fuzzyDL reasoner, an extension of [23], [24], [25], [26].”