A conceptual clustering algorithm for database schema design

1994 ◽  
Vol 6 (3) ◽  
pp. 396-411 ◽  
Author(s):  
H.W. Beck ◽  
T. Anwar ◽  
S.B. Navathe
Keyword(s):  
Clustering Algorithm ◽  
Database Schema ◽  
Conceptual Clustering ◽  
Schema Design

An expert system with case-based reasoning for database schema design

1996 ◽  
Vol 18 (1) ◽  
pp. 83-95 ◽  
Author(s):  
Yong-Kee Paek ◽  
Jungyun Seo ◽  
Gil-Chang Kim
Keyword(s):  
Expert System ◽  
Database Schema ◽  
Case Based Reasoning ◽  
Schema Design ◽  
Case Based

Object Oriented Database Schema Design

OOIS 2001 ◽  
2001 ◽  
pp. 497-510 ◽  
Author(s):  
B. B. Meshram ◽  
T. R. Sontakke
Keyword(s):  
Object Oriented ◽  
Database Schema ◽  
Schema Design ◽  
Object Oriented Database

Database Schema Design: The Contribution of Natural Language Techniques

1994 ◽  
pp. 183-198 ◽  
Author(s):  
Jean-Noël Meunier ◽  
Gilles Levreau ◽  
Elisabeth Métais
Keyword(s):  
Natural Language ◽  
Database Schema ◽  
Schema Design

DL-LINK: A CONCEPTUAL CLUSTERING ALGORITHM FOR INDEXING DESCRIPTION LOGICS KNOWLEDGE BASES

2010 ◽  
Vol 04 (04) ◽  
pp. 453-486 ◽  
Author(s):  
CLAUDIA D'AMATO ◽  
STEFFEN STAAB ◽  
NICOLA FANIZZI ◽  
FLORIANA ESPOSITO
Keyword(s):  
Clustering Algorithm ◽  
Description Logics ◽  
Knowledge Bases ◽  
Conceptual Clustering ◽  
Retrieval Process ◽  
Crucial Issue ◽  
Efficient Resource ◽  
Tree Index ◽  
The Given ◽  
Resource Description

Efficient resource retrieval is a crucial issue, particularly when semantic resource descriptions are considered which enable the exploitation of reasoning services during the retrieval process. In this context, resources are commonly retrieved by checking if each available resource description satisfies the given query. This approach becomes inefficient with the increase of available resources. We propose a method for improving the retrieval process by constructing a tree index through a new conceptual clustering method for resources expressed as class definitions or as instances of classes in ontology languages. The available resource descriptions are located at the leaf nodes of the index, while inner nodes represent intensional descriptions (generalizations) of their child nodes. The retrieval is performed by following the tree branches whose nodes satisfy the query. Query answering time may be improved as the number of retrieval steps may be O(log n) in the best case.

Sign in / Sign up

Export Citation Format

Share Document