Managing Uncertainty and Vagueness in Description Logics for the Semantic Web

Lukasiewicz, Thomas; Straccia, Umberto

doi:10.2139/ssrn.3199411

Cited by 135 publications

(174 citation statements)

References 102 publications

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“…The relationship between possibilistic DLs and other uncertainty formalisms for DLs has been discussed in a survey paper [5]. One of the most important approaches that extend DLs with uncertainty reasoning are probabilistic DLs, such as the work presented in [4] which has a tool support [3].…”

Section: Related Workmentioning

confidence: 99%

“…Suppose we use possibilistic logic, then ax 1 means that "it is absolute certain that heart patients suffers from high blood pressure", ax 2 can be explained similarly, ax 3 says that "it is a little certain that heart patient are male pacemaker patient", ax 4 says "it is highly certain that heart patients have a private insurance", and finally ax 5 states that "it is quite certain that Tom is a pacemaker patient". Suppose we learn that Tom is a heart patient with degree 0.5 (ax 6 : (HeartP atient(T om),0.5)), i.e., it is somewhat certain that Tom is a heart patient, and we add this axiom to the ontology, then the ontology will become inconsistent.…”

Section: Introductionmentioning

confidence: 99%

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PossDL — A Possibilistic DL Reasoner for Uncertainty Reasoning and Inconsistency Handling

Pan

et al. 2010

Lecture Notes in Computer Science

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Abstract. Uncertainty reasoning and inconsistency handling are two important problems that often occur in the applications of the Semantic Web. Possibilistic description logics provide a flexible framework for representing and reasoning with ontologies where uncertain and/or inconsistent information exists. Based on our previous work, we develop a possibilistic description logic reasoner. Our demo will illustrate functionalities of our reasoner for various reasoning tasks that possibilistic description logics can provide.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PossDL — A Possibilistic DL Reasoner for Uncertainty Reasoning and Inconsistency Handling

Pan

et al. 2010

Lecture Notes in Computer Science

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“…A great variety of fuzzy DLs can be found in the literature (for two relevant surveys see [18,12]). In fact, fuzzy DLs have several degrees of freedom for defining their expressiveness.…”

Section: Introductionmentioning

confidence: 99%

How Fuzzy Is My Fuzzy Description Logic?

Borgwardt

Distel

Peñaloza

2012

Automated Reasoning

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Abstract. Fuzzy Description Logics (DLs) with t-norm semantics have been studied as a means for representing and reasoning with vague knowledge. Recent work has shown that even fairly inexpressive fuzzy DLs become undecidable for a wide variety of t-norms. We complement those results by providing a class of t-norms and an expressive fuzzy DL for which ontology consistency is linearly reducible to crisp reasoning, and thus has its same complexity. Surprisingly, in these same logics crisp models are insufficient for deciding fuzzy subsumption.

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“…Managing fuzzy knowledge is of great importance in many applications, like multimedia processing [5,6], decision making [7], negotiation [8], and more. For these reasons many fuzzy extensions to OWL and DLs have been proposed [9,10,11,12,13,14,15,16,17]. Using fuzzy DLs we can state axioms like GoodDoctor(a) = 0.8 and GoodDoctor(b) = 0.7 which capture the fact that "a" is a better doctor than "b".…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Extension to the OWL 2 RL Ontology Language

Stoilos

Venetis

Stamou

2015

The Computer Journal

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Fuzzy extensions to Description Logics (DLs) have gained considerable attention the last decade. So far most works on fuzzy DLs have focused on either very expressive languages, like fuzzy OWL and OWL 2, or on highly inexpressive ones, like fuzzy OWL 2 QL and fuzzy OWL 2 EL. To the best of our knowledge a fuzzy extension to the language OWL 2 RL has not been thoroughly studied so far. This language is very relevant since it combines both adequate expressive power as well as efficient reasoning algorithms which can be realised using rule-based (Datalog) technologies. In contrast to previous fuzzy extensions, a fuzzy extension of OWL 2 RL is not a straightforward task for the following reason. The main motivation of OWL 2 RL is that its axioms can be equivalently represented as Datalog rules. Hence, to achieve our goal we need to investigate which OWL 2 RL axioms when interpreted under the fuzzy setting can be transformed to equivalent fuzzy Datalog rules. We show that this is not, in general, possible for all axioms but we show that this "issue" can to a large extent be alleviated. Moreover, we have performed an experimental evaluation with many well-known ontologies which showed that such axioms are not used so often in practice.

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Managing Uncertainty and Vagueness in Description Logics for the Semantic Web

Cited by 135 publications

References 102 publications

PossDL — A Possibilistic DL Reasoner for Uncertainty Reasoning and Inconsistency Handling

PossDL — A Possibilistic DL Reasoner for Uncertainty Reasoning and Inconsistency Handling

How Fuzzy Is My Fuzzy Description Logic?

A Fuzzy Extension to the OWL 2 RL Ontology Language

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