Subject-oriented semantic knowledge warehouse (SSKW) to support cognitive DSS

Memon, T; Lu, J; Hussain, FK; Rauniyar, R

Subject-oriented semantic knowledge warehouse (SSKW) to support cognitive DSS

Memon, T Lu, J

Hussain, FK

Rauniyar, R

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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), 2013, 8185 LNCS pp. 291 - 299
Issue Date:: 2013-11-05

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Field	Value	Language
dc.contributor.author	Memon, T	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Hussain, FK https://orcid.org/0000-0003-1513-8072	en_US
dc.contributor.author	Rauniyar, R	en_US
dc.date.issued	2013-11-05	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 8185 LNCS pp. 291 - 299	en_US
dc.identifier.isbn	9783642410291	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29570
dc.description.abstract	The communication between cognitive DSS and data warehouse tends to be inefficient due to their contradictory knowledge/data oriented nature. Data-to-knowledge conversion requires specialized techniques, whereas knowledge-to-data conversion results in loss of knowledge. To address these issues, a subject-oriented semantic knowledge warehouse (SSKW) is proposed, to provide relevant and precise knowledge to CDSS. The SSKW consists of: a) object/process/event/relationship (OPER) model to store domain knowledge in a unified fashion; and, b) a subjective view database, containing opinions of stakeholders about various OPER knowledge elements. A case study to compare the performance of the SSKW-based CDSS against a DW-based CDSS is presented. The results show that SSKW improves communication efficiency, provides relevant and precise domain knowledge to CDSS in less decision cycles, minimizes the loss of knowledge, and helps decision maker to quickly grasp the decision situation through its human-centric nature. © 2013 Springer-Verlag.	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	10.1007/978-3-642-41030-7_20	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Subject-oriented semantic knowledge warehouse (SSKW) to support cognitive DSS	en_US
dc.type	Conference Proceeding
utslib.citation.volume	8185 LNCS	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Graz, Austria	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 Computer Science
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	8185 LNCS	en_US

Abstract:

The communication between cognitive DSS and data warehouse tends to be inefficient due to their contradictory knowledge/data oriented nature. Data-to-knowledge conversion requires specialized techniques, whereas knowledge-to-data conversion results in loss of knowledge. To address these issues, a subject-oriented semantic knowledge warehouse (SSKW) is proposed, to provide relevant and precise knowledge to CDSS. The SSKW consists of: a) object/process/event/relationship (OPER) model to store domain knowledge in a unified fashion; and, b) a subjective view database, containing opinions of stakeholders about various OPER knowledge elements. A case study to compare the performance of the SSKW-based CDSS against a DW-based CDSS is presented. The results show that SSKW improves communication efficiency, provides relevant and precise domain knowledge to CDSS in less decision cycles, minimizes the loss of knowledge, and helps decision maker to quickly grasp the decision situation through its human-centric nature. © 2013 Springer-Verlag.

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http://hdl.handle.net/10453/29570