GrandBase: generating actionable knowledge from Big Data

Fang, XS; Sheng, QZ; Wang, X; Ngu, AHH; Zhang, Y

GrandBase: generating actionable knowledge from Big Data

Fang, XS Sheng, QZ Wang, X

Ngu, AHH Zhang, Y

Permalink

Publisher:: Emerald
Publication Type:: Journal Article
Citation:: PSU Research Review, 2017, 1, (2), pp. 105-126
Issue Date:: 2017

Closed Access

	Filename	Description	Size
	10-1108_PRR-01-2017-0005.pdf		755.27 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Fang, XS
dc.contributor.author	Sheng, QZ
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9582-3445
dc.contributor.author	Ngu, AHH
dc.contributor.author	Zhang, Y
dc.date.accessioned	2022-08-19T02:33:15Z
dc.date.available	2022-08-19T02:33:15Z
dc.date.issued	2017
dc.identifier.citation	PSU Research Review, 2017, 1, (2), pp. 105-126
dc.identifier.issn	2399-1747
dc.identifier.issn	2399-1747
dc.identifier.uri	http://hdl.handle.net/10453/160491
dc.description.abstract	Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.
dc.language	en
dc.publisher	Emerald
dc.relation.ispartof	PSU Research Review
dc.relation.isbasedon	10.1108/PRR-01-2017-0005
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	GrandBase: generating actionable knowledge from Big Data
dc.type	Journal Article
utslib.citation.volume	1
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
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-19T02:33:14Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	1
utslib.citation.issue	2

Abstract:

Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.

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

http://hdl.handle.net/10453/160491