Framework of computational intelligence-enhanced knowledge base construction: Methodology and a case of gene-related cardiovascular disease

Zhang, Y; Wu, M; Lin, H; Tipper, S; Grosser, M; Zhang, G; Lu, J

Framework of computational intelligence-enhanced knowledge base construction: Methodology and a case of gene-related cardiovascular disease

Zhang, Y

Wu, M

Lin, H Tipper, S Grosser, M Zhang, G

Lu, J

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Publisher:: ATLANTIS PRESS
Publication Type:: Journal Article
Citation:: International Journal of Computational Intelligence Systems, 2020, 13, (1), pp. 1109-1119
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Wu, M https://orcid.org/0000-0003-3956-7808
dc.contributor.author	Lin, H
dc.contributor.author	Tipper, S
dc.contributor.author	Grosser, M
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date.accessioned	2021-08-11T10:15:38Z
dc.date.available	2021-08-11T10:15:38Z
dc.date.issued	2020-01-01
dc.identifier.citation	International Journal of Computational Intelligence Systems, 2020, 13, (1), pp. 1109-1119
dc.identifier.issn	1875-6891
dc.identifier.issn	1875-6883
dc.identifier.uri	http://hdl.handle.net/10453/150102
dc.description.abstract	Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques through the integration of intelligent bibliometrics—e.g., co-occurrence analysis is used for profiling research topics/domains and identifying key players, and recommending potential collaborators based on the incorporation of a link prediction approach; an approach of scientific evolutionary pathways is exploited to trace the evolution of research topics; and a search engine incorporating with fuzzy logics, word embedding, and genetic algorithm is developed for knowledge searching and ranking. Aiming to examine and demonstrate the reliability of the proposed framework, a case of gene-related cardiovascular diseases is selected, and a knowledge base is constructed, with the validation of domain experts.
dc.language	English
dc.publisher	ATLANTIS PRESS
dc.relation	http://purl.org/au-research/grants/arc/DE190100994
dc.relation.ispartof	International Journal of Computational Intelligence Systems
dc.relation.isbasedon	10.2991/ijcis.d.200728.001
dc.rights	©2020The Authors. Published by Atlantis Press SARL.This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	08 Information and Computing Sciences
dc.title	Framework of computational intelligence-enhanced knowledge base construction: Methodology and a case of gene-related cardiovascular disease
dc.type	Journal Article
utslib.citation.volume	13
utslib.for	0806 Information Systems
utslib.for	08 Information and Computing Sciences
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	recently_added	*
dc.date.updated	2021-08-11T10:15:34Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	13
utslib.citation.issue	1

Abstract:

Knowledge base construction (KBC) aims to populate knowledge bases with high-quality information from unstructured data but how to effectively conduct KBC from scientific documents with limited preknowledge is still elusive. This paper proposes a KBC framework by applying computational intelligent techniques through the integration of intelligent bibliometrics—e.g., co-occurrence analysis is used for profiling research topics/domains and identifying key players, and recommending potential collaborators based on the incorporation of a link prediction approach; an approach of scientific evolutionary pathways is exploited to trace the evolution of research topics; and a search engine incorporating with fuzzy logics, word embedding, and genetic algorithm is developed for knowledge searching and ranking. Aiming to examine and demonstrate the reliability of the proposed framework, a case of gene-related cardiovascular diseases is selected, and a knowledge base is constructed, with the validation of domain experts.

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

http://hdl.handle.net/10453/150102