Measuring Inconsistency through Minimal Proofs

Jabbour, Saïd; Raddaoui, Badran

doi:10.1007/978-3-642-39091-3_25

Cited by 12 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many inconsistency measures over knowledge bases have been proposed [3,5,6,9,10,11,12,14,16]. The intuition is: the higher the amount of inconsistency in the knowledge base, the greater the number returned by the inconsistency measure (the range of an inconsistency measure is taken to be R + ∪ {∞}, so that the range is totally ordered and 0 is the least element).…”

Section: Introductionmentioning

confidence: 99%

Revisiting Postulates for Inconsistency Measures

Besnard

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible Abstract. Postulates for inconsistency measures are examined, the set of postulates due to Hunter and Konieczny being the starting point. Objections are raised against a few individual postulates. More general shortcomings are discussed and a new series of postulates is introduced.

show abstract

Section: Introductionmentioning

confidence: 99%

Revisiting Postulates for Inconsistency Measures

Besnard

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…vectorial measures [22], deduction-based measures [14], and measures based on maximum consistent subsets [31]), we believe that the above results will extend in a straightforward way. Similarly, we believe that it is straightforward to extend the above results to see the difference with the proposals that give a higher value for inconsistency to smaller minimal inconsistent sets of formulae (e.g.…”

Section: Definition 28mentioning

confidence: 87%

Analysing inconsistent information using distance-based measures

Grant¹,

Hunter²

2017

International Journal of Approximate Reasoning

View full text Add to dashboard Cite

There have been a number of proposals for measuring inconsistency in a knowledgebase (i.e. a set of logical formulae). These include measures that consider the minimally inconsistent subsets of the knowledgebase, and measures that consider the paraconsistent models (3 or 4 valued models) of the knowledgebase. In this paper, we present a new approach that considers the amount by which each formula has to be weakened in order for the knowledgebase to be consistent. This approach is based on ideas of knowledge merging by Konienczny and Pino-Perez. We show that this approach gives us measures that are different from existing measures, that have desirable properties, and that can take the significance of inconsistencies into account. The latter is useful when we want to differentiate between inconsistencies that have minor significance from inconsistencies that have major significance. We also show how our measures are potentially useful in applications such as evaluating violations of integrity constraints in databases and for deciding how to act on inconsistency.

show abstract

“…We might mention, for example, inconsistent truth assignments in Belnap [Bel77], LP m [Pri91] or Quasi-classical [BH95] paraconsistent models as well as the notion of minimal proofs [JR13]. However, MISes are often seen as the purest form of formula-centric inconsistency [Rei87, HK10, MLJ12] since they express inconsistency by means of conicting formulae.…”

Section: Computing All Mises From Musesmentioning

confidence: 99%

“…from [XM12]; and the I Pm measure from [JR13]. Each circle indicates a particular category of measure where the intersections indicate measures which fall into multiple categories.…”

Section: Measuring the Inconsistency Of Formulaementioning

confidence: 99%

Computational approaches to finding and measuring inconsistency in arbitrary knowledge bases

McAreavey

Liu

Miller

2014

International Journal of Approximate Reasoning

View full text Add to dashboard Cite

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. There is extensive theoretical work on measures of inconsistency for arbitrary formulae in knowledge bases. Many of these are dened in terms of the set of minimal inconsistent subsets (MISes) of the base. However, few have been implemented or experimentally evaluated to support their viability, since computing all MISes is intractable in the worst case. Fortunately, recent work on a related problem of minimal unsatisable sets of clauses (MUSes) oers a viable solution in many cases. In this paper, we begin by drawing connections between MISes and MUSes through algorithms based on a MUS generalization approach and a new optimized MUS transformation approach to nding MISes. We implement these algorithms, along with a selection of existing measures for at and stratied knowledge bases, in a tool called mimus. We then carry out an extensive experimental evaluation of mimus using randomly generated arbitrary knowledge bases. We conclude that these measures are viable for many large and complex random instances. Moreover, they represent a practical and intuitive tool for inconsistency handling.

show abstract

Measuring Inconsistency through Minimal Proofs

Cited by 12 publications

References 20 publications

Revisiting Postulates for Inconsistency Measures

Revisiting Postulates for Inconsistency Measures

Analysing inconsistent information using distance-based measures

Computational approaches to finding and measuring inconsistency in arbitrary knowledge bases

Contact Info

Product

Resources

About