Integration of ontology data through learning instance matching

Wang, C; Lu, J; Zhang, G

Integration of ontology data through learning instance matching

Wang, C Lu, J

Zhang, G

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), WI'06, 2006, pp. 536 - 539
Issue Date:: 2006-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (87.84 kB)

View on publisher's site

View statistics

Full metadata record

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	2006-01-01	en_US
dc.identifier.citation	Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), WI'06, 2006, pp. 536 - 539	en_US
dc.identifier.isbn	0769527477	en_US
dc.identifier.isbn	9780769527475	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2459
dc.description.abstract	Information integration with the aid of ontology can roughly be divided into two levels: schema level and data level. Most research has been focused on the schema level, i.e., mapping/matching concepts and properties in different ontologies with each other. However, the data level integration is equally important, especially in the decentralized Semantic Web environment. Noticing that ontology data (in the form of instances of concepts) from different sources often have different perspectives and may overlap with each other, we develop a matching method that utilizes the features of ontology and employs the machine learning approach to integrate those instances. By exploring ontology features, this method performs better than other general methods, which is revealed in our experiments. Through the process that implements the matching method, ontology data can be integrated together to offer more sophisticated services. © 2006 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings), WI'06	en_US
dc.relation.isbasedon	10.1109/WI.2006.100	en_US
dc.title	Integration of ontology data through learning instance matching	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	HK, China	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:

Information integration with the aid of ontology can roughly be divided into two levels: schema level and data level. Most research has been focused on the schema level, i.e., mapping/matching concepts and properties in different ontologies with each other. However, the data level integration is equally important, especially in the decentralized Semantic Web environment. Noticing that ontology data (in the form of instances of concepts) from different sources often have different perspectives and may overlap with each other, we develop a matching method that utilizes the features of ontology and employs the machine learning approach to integrate those instances. By exploring ontology features, this method performs better than other general methods, which is revealed in our experiments. Through the process that implements the matching method, ontology data can be integrated together to offer more sophisticated services. © 2006 IEEE.

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

http://hdl.handle.net/10453/2459