Measuring and repairing inconsistency in knowledge bases with graded truth

Muiòo, David Picado

doi:10.1016/j.fss.2011.10.012

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…Being the fuzzy realm a matter of degrees, a number of papers have focused on measuring the degree of inconsistency of a set of fuzzy rules, and a number of different inconsistency indices have been introduced. For instance Kubler et al 13 introduce the so‐called knowledge‐based consistency index for deriving priorities from fuzzy pairwise comparison matrices in multiple‐criteria decision‐making problems; other approaches introduce means for both measuring and repairing inconsistency, for example, Picado‐Muiño 14 presents a family of measures aimed at determining the amount of inconsistency in knowledge bases with graded truth and considers minimal adjustments in the truth degrees of the propositions necessary to make the knowledge‐base to be consistent within a given frame (in that case the Łukasiewicz semantics); last but not least 15 deals with the definition of measures of inconsistency in the residuated‐logic‐programming paradigm under the fuzzy answer set semantics and provides a soft mechanism to control the amount of information inferred, thus controlling the inconsistencies by modifying slightly the truth values of some rules. The number of possible measures of inconsistency that can be found in the literature somehow suggests the existence of a problem with inconsistency in a fuzzy setting, namely, its definition: There is not a consensus on how to interpret inconsistency in a fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

A measure of consistency for fuzzy logic theories

Madrid

Ojeda‐Aciego

2021

Math Methods in App Sciences

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Fuzzy logic has shown to be a suitable framework to handle contradictions in which, unsurprisingly, the notion of inconsistency can be defined in different ways. This paper starts with a short survey of different ways to define the notion of inconsistency in fuzzy logic systems. As a result, we provide a first notion of inconsistency by means of the absence of models. Subsequently, we define two measures of consistency that belong purely to the fuzzy paradigm; in the sense that both measures coincide with the crisp notion of consistency when the set of truth values is {0, 1}. Accordingly, we can state that the two provided measures of consistence are notions of consistence based on degrees, bringing back the spirit of fuzzy logic into the notion of consistency.

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Section: Introductionmentioning

confidence: 99%

A measure of consistency for fuzzy logic theories

Madrid

Ojeda‐Aciego

2021

Math Methods in App Sciences

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“…Onceinconsistenciesarefoundinaknowledgebase,theproblembecomeshowto dealwiththem.Severalapproachestodealingwithknowninconsistencieshavebeen used.Someinvolvealteringtheknowledgebasesoastoremovetheinconsistency, such as the "graded truth" in Picado (2012); others involve altering the inferences performedusingtheknowledgeortherulesetitselftotaketheinconsistenciesinto account,aswiththe"beliefrevision"inDecker&deJuan-Marîn(2013).Ourpaper focusesonlyonthedetectionofaninconsistency,asanecessaryfirststep.…”

Section: Introductionmentioning

confidence: 99%

Specifying Constraints for Detecting Inconsistencies in A Conceptual Graph Knowledge Base

Kassos

Delugach

2017

International Journal of Conceptual Structures and Smart Applications

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This paper proposes a strategy for representing constraints in a conceptual graph knowledgebase.Wedescribeasetoftechniquesforusingtheseconstraintstodetect inconsistenciesinaknowledgebasebyfindingsetsofnodesthatareinconsistentwith theseconstraints.Thedetectionmethodisdesignedtobeefficient.Analgorithmwas developedandanalyzedanditscomputationalcomplexitywasfoundtobepolynomial withrespecttoknowledgebasesizeandnumberofchildnodesforeachconstraintnode.

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