Online from: 2005
Subject Area: Information and Knowledge Management
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|Title:||Web ontology data matching for integration: method and framework|
|Author(s):||Chao Wang, (Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia), Jie Lu, (Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia), Guangquan Zhang, (Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia)|
|Citation:||Chao Wang, Jie Lu, Guangquan Zhang, (2009) "Web ontology data matching for integration: method and framework", International Journal of Web Information Systems, Vol. 5 Iss: 2, pp.220 - 238|
|Keywords:||Data collection, Data structures, Semantics|
|Article type:||Research paper|
|DOI:||10.1108/17440080910968463 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||The work presented in this paper was partly supported by Australian Research Council (ARC) under discovery project DP0880739.|
Purpose – Matching relevant ontology data for integration is vitally important as the amount of ontology data increases along with the evolving Semantic web, in which data are published from different individuals or organizations in a decentralized environment. For any domain that has developed a suitable ontology, its ontology annotated data (or simply ontology data) from different sources often overlaps and needs to be integrated. The purpose of this paper is to develop intelligent web ontology data matching method and framework for data integration.
Design/methodology/approach – This paper develops an intelligent matching method to solve the issue of ontology data matching. Based on the matching method, it also proposes a flexible peer-to-peer framework to address the issue of ontology data integration in a distributed Semantic web environment.
Findings – The proposed matching method is different from existing data matching or merging methods applied to data warehouse in that it employs a machine learning approach and more similarity measurements by exploring ontology features.
Research limitations/implications – The proposed method and framework will be further tested for some more complicated real cases in the future.
Originality/value – The experiments show that this proposed intelligent matching method increases ontology data matching accuracy.
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