An Optimization for Query Answering on ALC Database

Pothipruk, P. and Governatori, G (2005). An Optimization for Query Answering on ALC Database. In: G. Dobbie and J. Bailey, Proceedings of 17th Australasian Database Conference (ADC2006). 17th Australasian Database Conference (ADC2006), Hobart, Tasmania, Australia, (129-137). 16-19 January, 2006.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
ADC06-Eprint.pdf ADC06-Eprint.pdf application/pdf 524.25KB 240
Author Pothipruk, P.
Governatori, G
Title of paper An Optimization for Query Answering on ALC Database
Conference Paper Type Fully Published Paper
Conference name 17th Australasian Database Conference (ADC2006)    (ERA 2010 Rank B)
Conference location Hobart, Tasmania, Australia
Conference dates 16-19 January, 2006
Proceedings title Proceedings of 17th Australasian Database Conference (ADC2006)
Editor G. Dobbie
J. Bailey
Place published New South Wales
Publisher Australian Computer Society Inc.
Publication date 2005
Volume number 49
Issue number 1
ISBN 1-920-68231-7
Start page 129
End page 137
Total pages 9
Collection year 2006
Language eng
Abstract/Summary Query answering over OWLs and RDFs on the Semantic Web is, in general, a deductive process. To this end, OWL, a family of web ontology languages based on description logic, has been proposed as the language for the Semantic Web. However, reasoning even on ALC, a description logic weaker than OWL, faces efficiency problem. To obviate this problem, at least for ALC, we propose a partition approach that improves the efficiency by splitting the search space into independent Aboxes. Each partition class, i.e., an Abox, can be queried independently. The answer to a query is the simple combination of the answers from each Abox. We prove the correctness of this approach and we outline how to represent compactly the content of each independent Abox. This work can be seen as an optimization for querying a deductive semistructured database.
Subjects 280103 Information Storage, Retrieval and Management
280403 Logics and Meanings of Programs
280213 Other Artificial Intelligence
280108 Database Management
E1
280100 Information Systems
780100 Non-oriented Research
Keyword description logic
query optimization
Web database
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
Versions
Version Filter Type
Citation counts: Google Scholar Search Google Scholar
Access Statistics: 204 Abstract Views, 240 File Downloads  -  Detailed Statistics
Created: Mon, 31 Oct 2005, 10:00:00 EST by Pakornpong Pothipruk on behalf of School of Information Technol and Elec Engineering