A framework for capturing domain knowledge via the web

Wang, C; Lu, J; Zhang, G

A framework for capturing domain knowledge via the web

Wang, C Lu, J

Zhang, G

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Publication Type:: Conference Proceeding
Citation:: AusWeb05: 11th Australasian World Wide Web Conference, 2005
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Wang, C	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	AusWeb05: 11th Australasian World Wide Web Conference, 2005	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1895
dc.description.abstract	Domain knowledge can be formalized and represented by ontologies, which play an important role in the realization of the Semantic Web. However, since the acquisition of knowledge from certain domains usually requires deep involvement of qualified domain experts, construction of such ontologies is difficult and costly, even with the availability of dedicated languages and ontology editing tools. Some effect has been made to reduce this involvement by introducing a general paradigm of automatic domain knowledge learning from various sources. To make this paradigm more specific and practical, this paper proposes a framework for capturing domain knowledge through raw domain data available over the Web. This framework consists of three dedicated parts: data collection, pre-processing and mining, where mining part performs core task of the framework. Each part can be designed with specific optimized methods. The preliminary implementation of certain parts has shown it is able to capture the knowledge of electronic product taxonomy via the Web. © 2005. Chao Wang, Jie Lu & Guangquan Zhang.	en_US
dc.relation.ispartof	AusWeb05: 11th Australasian World Wide Web Conference	en_US
dc.title	A framework for capturing domain knowledge via the web	en_US
dc.type	Conference Proceeding
utslib.for	0804 Data Format	en_US
dc.location.activity	Gold Coast, Aust	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	open_access
pubs.publication-status	Published	en_US

Abstract:

Domain knowledge can be formalized and represented by ontologies, which play an important role in the realization of the Semantic Web. However, since the acquisition of knowledge from certain domains usually requires deep involvement of qualified domain experts, construction of such ontologies is difficult and costly, even with the availability of dedicated languages and ontology editing tools. Some effect has been made to reduce this involvement by introducing a general paradigm of automatic domain knowledge learning from various sources. To make this paradigm more specific and practical, this paper proposes a framework for capturing domain knowledge through raw domain data available over the Web. This framework consists of three dedicated parts: data collection, pre-processing and mining, where mining part performs core task of the framework. Each part can be designed with specific optimized methods. The preliminary implementation of certain parts has shown it is able to capture the knowledge of electronic product taxonomy via the Web. © 2005. Chao Wang, Jie Lu & Guangquan Zhang.

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http://hdl.handle.net/10453/1895