DMCA
Data abstraction approach for query relaxation (2000)
| Venue: | Information and Software Technology |
| Citations: | 3 - 0 self |
BibTeX
@ARTICLE{Huh00dataabstraction,
author = {S. -y. Huh and K. -h. Moon and H. Lee},
title = {Data abstraction approach for query relaxation},
journal = {Information and Software Technology},
year = {2000},
volume = {42},
pages = {407--418}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Since a query language is used as a handy tool to obtain information from a database, users want more user-friendly and fault-tolerant query interfaces. When a query search condition does not match with the underlying database, users would rather receive approximate answers than null information by relaxing the condition. They also prefer a less rigid querying structure, one which allows for vagueness in composing queries, and want the system to understand the intent behind a query. This paper presents a data abstraction approach to facilitate the development of such a fault-tolerant and intelligent query processing system. It specifically proposes a knowledge abstraction database that adopts a multilevel knowledge representation scheme called the knowledge abstraction hierarchy. Furthermore, the knowledge abstraction database extracts semantic data relationships from the underlying database and supports query relaxation using query generalization and specialization steps. Thus, it can broaden the search scope of original queries to retrieve neighborhood information and help users to pose conceptually abstract queries. Specifically, four types of vague queries are discussed, including approximate selection, approximate join, conceptual selection and conceptual join. � 2000 Elsevier Science B.V. All rights reserved.
Keyphrases
data abstraction approach query relaxation underlying database knowledge abstraction database handy tool null information help user elsevier science b.v. right query generalization knowledge abstraction hierarchy rigid querying structure fault-tolerant query interface search scope original query approximate answer approximate join conceptual selection neighborhood information conceptual join semantic data relationship vague query query search condition approximate selection support query relaxation specialization step intelligent query processing system multilevel knowledge representation scheme query language abstract query
