Possibility theory and formal concept analysis: Context decomposition and uncertainty handling

Djouadi, Y; Dubois, D; Prade, H

Possibility theory and formal concept analysis: Context decomposition and uncertainty handling

Djouadi, Y Dubois, D Prade, H

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6178 LNAI pp. 260 - 269
Issue Date:: 2010-07-29

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Field	Value	Language
dc.contributor.author	Djouadi, Y	en_US
dc.contributor.author	Dubois, D	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.date.issued	2010-07-29	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6178 LNAI pp. 260 - 269	en_US
dc.identifier.isbn	3642140483	en_US
dc.identifier.isbn	9783642140488	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32896
dc.description.abstract	Formal Concept Analysis uses a simple representation framework called 'formal context'. In the classical setting, a formal context specifies existing Boolean relationships between a set of objects and their corresponding properties. Formal concepts are then defined as pairs consisting of a set of objects and a set of properties that mutually characterize each other through a Galois connection. Another Galois connection is also introduced in this setting on the basis of operators induced by a recent possibility theory reading of Formal Concept Analysis. It is shown that this second Galois connection enables us to characterize independent sub-contexts inside the formal context. The second part of the paper discusses an extension of Formal Concept Analysis that has not been much studied, namely the situation where one may be uncertain on the fact that an object possesses or not a Boolean property. Uncertainty is here represented in the possibilistic representation framework. © 2010 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-14049-5_27	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Possibility theory and formal concept analysis: Context decomposition and uncertainty handling	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6178 LNAI	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6178 LNAI	en_US

Abstract:

Formal Concept Analysis uses a simple representation framework called 'formal context'. In the classical setting, a formal context specifies existing Boolean relationships between a set of objects and their corresponding properties. Formal concepts are then defined as pairs consisting of a set of objects and a set of properties that mutually characterize each other through a Galois connection. Another Galois connection is also introduced in this setting on the basis of operators induced by a recent possibility theory reading of Formal Concept Analysis. It is shown that this second Galois connection enables us to characterize independent sub-contexts inside the formal context. The second part of the paper discusses an extension of Formal Concept Analysis that has not been much studied, namely the situation where one may be uncertain on the fact that an object possesses or not a Boolean property. Uncertainty is here represented in the possibilistic representation framework. © 2010 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/32896