A framework of Ontology Guided Data Linkage for evidence based knowledge extraction and information sharing

Gollapalli, Mohammed and Li, Xue (2013). A framework of Ontology Guided Data Linkage for evidence based knowledge extraction and information sharing. In: 2013 IEEE 29th International Conference on Data Engineering Workshops, ICDEW 2013. 2013 IEEE 29th International Conference on Data Engineering Workshops, ICDEW 2013, Brisbane, QLD, Australia, (294-297). 8-11 April 2013. doi:10.1109/ICDEW.2013.6547467

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads

Author Gollapalli, Mohammed
Li, Xue
Title of paper A framework of Ontology Guided Data Linkage for evidence based knowledge extraction and information sharing
Conference name 2013 IEEE 29th International Conference on Data Engineering Workshops, ICDEW 2013
Conference location Brisbane, QLD, Australia
Conference dates 8-11 April 2013
Proceedings title 2013 IEEE 29th International Conference on Data Engineering Workshops, ICDEW 2013   Check publisher's open access policy
Journal name Proceedings - International Conference on Data Engineering   Check publisher's open access policy
Series Proceedings - International Conference on Data Engineering
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2013
Sub-type Fully published paper
DOI 10.1109/ICDEW.2013.6547467
Open Access Status
ISBN 9781467353021
ISSN 1084-4627
Start page 294
End page 297
Total pages 4
Collection year 2014
Language eng
Abstract/Summary There has been a surge of interests in developing probabilistic techniques for linking semantic equivalent datasets. The key objective is to transform the structure of the induced data into a concise synopsis. Current techniques primarily focus on performing pair-wise attribute matching and pay little attention in discovering direct and weighted correlations among ontological clusters through multi-faceted classification. In this research, we introduce a novel Ontology Guided Data Linkage (OGDL) framework for self-organising and discovering schema structures through constructing a hierarchical cluster mapping trees. Furthermore, we extend our OGDL framework by introducing a novel faceted search engine for semantic interoperability of data and subsequent decision support analysis, and use it to map fast cluster browsing, user friendly querying and semantic reasoning learning needs.
Keyword Data linkage
Knowledge discovery
Ontology mapping
Query processing
Semantic reasoning
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Thu, 28 Nov 2013, 18:11:14 EST by System User on behalf of School of Information Technol and Elec Engineering