<div class="csl-bib-body">
<div class="csl-entry">Weinzierl, A. (2014). <i>Inconsistency management under preferences for multi-context systems and extensions</i> [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2014.25013</div>
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dc.identifier.uri
https://doi.org/10.34726/hss.2014.25013
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/8225
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dc.description
Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers
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dc.description
Zsfassung in dt. Sprache
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dc.description.abstract
Multi-Context Systems (MCS) are a versatile and powerful framework for heterogeneous, nonmonotonic knowledge-integration. MCS allow information exchange between legacy information systems, i.e., knowledge bases. Inconsistency occurs easily in such scenarios since it is impossible to foresee all effects and consequences of the information exchange. Inconsistency makes an MCS useless, just as in other formal systems; thus, inconsistency management is a major issue. Resolving inconsistency by purely technical means is guaranteed to yield a consistent system, but it can easily result in a system where the remaining information exchange leads to unwanted or even dangerous conclusions. Consider, for example, an MCS that is employed in a hospital and the billing subsystem became inconsistent because of a patient with insufficient insurance. Automatically resolving the inconsistency by declaring the patient to be healthy and sending her home instead of administering proper treatment surely is a solution, but it hardly is a valid one. On the other hand, manually resolving all inconsistencies is not feasible since there usually exist too many possible resolutions to consider each of them individually. This thesis therefore addresses the issues of inconsistency management in MCS with a focus on using preferences to identify and automatically select preferred and valid resolutions of inconsistency, to aid a human operator by significantly reducing the number of possible resolutions to consider. The novelty of this approach is on the one hand a technique to enable meta-reasoning about inconsistency resolutions within the MCS framework, i.e., preferences expressed in any knowledge formalism can be incorporated to identify preferred resolutions and filter unwanted ones. On the other hand, an extension of the MCS framework is introduced to enable the use of legacy inconsistency management methods directly at each information system. This thesis consists of three main parts. The first investigates basic notions to identify and explain inconsistency in MCS. These notions are called diagnosis and explanation of inconsistency. Refined notions are investigated and shown to be reducible to the basic notions, and splitting-set based conditions are analyzed which allow to modularly obtain diagnoses and explanations from parts of a given MCS. Finally, a logic program is given that computes all explanations of an MCS. The second part of this thesis is dedicated to the identification and selection of most-preferred diagnoses and the filtering of unwanted diagnoses. Several transformation-based approaches are introduced which allow a transformed MCS to reason about the diagnoses of the original MCS, i.e., these approaches enable meta-reasoning on diagnoses in MCS. The necessary extended notions of diagnosis are shown to be of the same complexity as the basic notion, except for one, which is of higher complexity but still shown to be worst-case optimal. Therefore, the new meta-reasoning approach incurs no unnecessary complexity-wise cost. In the third part, the MCS framework is extended to incorporate existing, formalism-specific methods of inconsistency management (e.g., belief revision for classical logics, or updates for logic programs). In such an MCS where each knowledge base comes with local inconsistency management, the overall system can only become inconsistent due to cyclic information exchange. Furthermore, the extended framework is reducible to ordinary MCS and checking consistency in the extended framework is of the same computational complexity as in ordinary MCS.
en
dc.language
English
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dc.language.iso
en
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Computer Science
en
dc.subject
Artificial Intelligence
en
dc.subject
Knowledge Representation and Reasoning
en
dc.subject
Multi-Context Systems
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dc.title
Inconsistency management under preferences for multi-context systems and extensions
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dc.title.alternative
Konsistenz-Management unter Praeferenzen fuer Multi-Kontext Systeme und Erweiterungen
de
dc.type
Thesis
en
dc.type
Hochschulschrift
de
dc.rights.license
In Copyright
en
dc.rights.license
Urheberrechtsschutz
de
dc.identifier.doi
10.34726/hss.2014.25013
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dc.contributor.affiliation
TU Wien, Österreich
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dc.rights.holder
Antonius Weinzierl
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tuw.version
vor
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tuw.thesisinformation
Technische Universität Wien
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dc.contributor.assistant
Fink, Michael
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tuw.publication.orgunit
E184 - Institut für Informationssysteme
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dc.type.qualificationlevel
Doctoral
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dc.identifier.libraryid
AC12121980
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dc.description.numberOfPages
215
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dc.identifier.urn
urn:nbn:at:at-ubtuw:1-67176
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dc.thesistype
Dissertation
de
dc.thesistype
Dissertation
en
tuw.author.orcid
0000-0003-2040-6123
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dc.rights.identifier
In Copyright
en
dc.rights.identifier
Urheberrechtsschutz
de
tuw.advisor.staffStatus
staff
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tuw.assistant.staffStatus
staff
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tuw.advisor.orcid
0000-0001-6003-6345
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item.languageiso639-1
en
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item.fulltext
with Fulltext
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item.openaccessfulltext
Open Access
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item.mimetype
application/pdf
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item.openairetype
doctoral thesis
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item.grantfulltext
open
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item.openairecristype
http://purl.org/coar/resource_type/c_db06
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item.cerifentitytype
Publications
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crisitem.author.dept
E192-03 - Forschungsbereich Knowledge Based Systems