Knowledge merging under multiple attributes

Wei, B; Jin, Z; Zowghi, D

Knowledge merging under multiple attributes

Wei, B Jin, Z Zowghi, D

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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, 6291 LNAI pp. 555 - 560
Issue Date:: 2010-11-08

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Field	Value	Language
dc.contributor.author	Wei, B	en_US
dc.contributor.author	Jin, Z	en_US
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155	en_US
dc.date.issued	2010-11-08	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6291 LNAI pp. 555 - 560	en_US
dc.identifier.isbn	3642152791	en_US
dc.identifier.isbn	9783642152795	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12974
dc.description.abstract	Knowledge merging is the process of synthesizing multiple knowledge models into a common model. Available methods concentrate on resolving conflicting knowledge. While, we argue that besides the inconsistency, some other attributes may also affect the resulting knowledge model. This paper proposes an approach for knowledge merging under multiple attributes, i.e. Consistency and Relevance. This approach introduces the discrepancy between two knowledge models and defines different discrepancy functions for each attribute. An integrated distance function is used for assessing the candidate knowledge models. © 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	10.1007/978-3-642-15280-1_51	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Knowledge merging under multiple attributes	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6291 LNAI	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Belfast, Northern Ireland	en_US
dc.location.activity	Istanbul Turkey
dc.location.activity	Belfast, NORTH IRELAND
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Graduate Research School
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 Systems, Management and Leadership
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6291 LNAI	en_US

Abstract:

Knowledge merging is the process of synthesizing multiple knowledge models into a common model. Available methods concentrate on resolving conflicting knowledge. While, we argue that besides the inconsistency, some other attributes may also affect the resulting knowledge model. This paper proposes an approach for knowledge merging under multiple attributes, i.e. Consistency and Relevance. This approach introduces the discrepancy between two knowledge models and defines different discrepancy functions for each attribute. An integrated distance function is used for assessing the candidate knowledge models. © 2010 Springer-Verlag Berlin Heidelberg.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/12974