Kernel contraction

Hansson, Sven Ove

doi:10.2307/2275912

Cited by 137 publications

(147 citation statements)

References 8 publications

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“…Given a belief base and a part icular belief , the objective of contraction is to compute a subset of that fails to imp ly [24]. Kernel contraction is a part icular operation of contraction.…”

Section: Kernel Contractionmentioning

confidence: 99%

“…The kernel contraction has proved to satisfy the following postulates [24]: success, inclusion, coreretain ment and uniformity. The postulate success says that the retracted belief should not be believed after contraction unless it is a tautology.…”

Section: Definition 32 (Incision Function)mentioning

confidence: 99%

“…Then, ( ( ) . Fo llowing the defin ition of align ment incision function (24), there is some set such that ( ( ) ) . Let * + .…”

Section: { }mentioning

confidence: 99%

“…Otherwise, is labeled by . By definit ion, alignment incision function (24) intersects each -Align ment kernel. If we consider align ment kernel (21) to be the collection , it seems naturel to consider incisions functions as its Hitting sets.…”

Section: { } Which Is An -Alignment Kernel (Line 5)mentioning

confidence: 99%

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Kernel Contraction and Consolidation of Alignment under Ontology Change

Zahaf¹,

Malki²

2016

IJITCS

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Abstract-Alignment overco mes divergence in the specification of the semantics of vocabularies by different but overlapping ontologies. Therefore, it enhances semantic interoperability for many web based applications. However, ontology change following applications new requirements or new perception of domain knowledges can leads to undesirable knowledge such as inconsistent and therefore to a useless alignment. Ontologies and align ments are encoded in knowledge bases allowing applications to store only some explicit knowledge while they derive imp licit ones by applying reasoning services on these knowledge bases. This underlying representation of ontologies and align ments leads us to follow base revision theory to deal with align ment revision under ontology change. For that purpose, we adapt kernel contraction framework to design rational operators and to formulate the set of postulates that characterize each class of these operators. We demonstrate the connection between each class of operators and the set of postulates that characterize them. Finally, we present algorithms to compute align ment kernels and incision functions. Kernels are sets of correspondences responsible of undesirable knowledge following align ment semantics. Incision functions determine the sets of correspondences to eliminate in order to restore alignment consistency or to realize a successful contraction.

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Section: Kernel Contractionmentioning

confidence: 99%

Section: Definition 32 (Incision Function)mentioning

confidence: 99%

“…Then, ( ( ) . Fo llowing the defin ition of align ment incision function (24), there is some set such that ( ( ) ) . Let * + .…”

Section: { }mentioning

confidence: 99%

Section: { } Which Is An -Alignment Kernel (Line 5)mentioning

confidence: 99%

See 2 more Smart Citations

Kernel Contraction and Consolidation of Alignment under Ontology Change

Zahaf¹,

Malki²

2016

IJITCS

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“…Belief revision studies changes to knowledge bases as a response to epistemic inputs. Traditionally, such knowledge bases can be either belief sets (sets of formulas closed under consequence) [3,4] or belief bases [5,2] (which are not closed); since our end goal is to apply the results we obtain to real-world domains, here we focus on belief bases. In particular, as motivated by requirements (i)-(iv) above, our knowledge bases consist of logical formulas over which we apply argumentation-based reasoning and to which we couple a probabilistic model.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Belief revision in structured probabilistic argumentation

Shakarian

Simari

Moores

et al. 2015

Ann Math Artif Intell

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Abstract. In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -based on well-known ones developed for classical knowledge bases -that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates.

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On Remainder Equations

1997

Mathematical Logic Qtrly

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show abstract

Kernel contraction

Cited by 137 publications

References 8 publications

Kernel Contraction and Consolidation of Alignment under Ontology Change

Kernel Contraction and Consolidation of Alignment under Ontology Change

Belief revision in structured probabilistic argumentation

On Remainder Equations

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