Communications of the ACM
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
The Clio project: managing heterogeneity
ACM SIGMOD Record
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ROSE - Software Implementation of the Rough Set Theory
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
A New Framework to Assess Association Rules
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
XML data exchange: consistency and query answering
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clio grows up: from research prototype to industrial tool
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
XML data integration in SixP2P: a theoretical framework
DaMaP '08 Proceedings of the 2008 international workshop on Data management in peer-to-peer systems
Deriving strong association mining rules using a dependency criterion, the lift measure
International Journal of Data Analysis Techniques and Strategies
Computers in patient care: the promise and the challenge
Communications of the ACM
Pellet-HeaRT - proposal of an architecture for ontology systems with rules
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
A rule-based implementation of fuzzy tableau reasoning
RuleML'10 Proceedings of the 2010 international conference on Semantic web rules
An hybrid architecture integrating forward rules with fuzzy ontological reasoning
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Rewriting queries using view for RDF/RDFS-Based relational data integration
ICDCIT'05 Proceedings of the Second international conference on Distributed Computing and Internet Technology
On ranking production rules for rule-based systems with uncertainty
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
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An expert system is considered to be reliable if it generates reliable hypotheses. The quality of the hypotheses depends mainly on the effectiveness of system's knowledge base. This paper discusses the problem of designing effective knowledge bases for rule-based systems with uncertainty. The knowledge is acquired from aggregate data stored in various repositories. The data can differ, to some extent, both in syntax and in semantics. The first part of an algorithm for rules' generation and refinement operates by means of semantic data integration. It allows to join aggregate data from different repositories and generate strong production rules. The second part of the algorithm is based on a formal concept of the normal base form. For having the property of normality, a knowledge base has to be internally consistent and not redundant. In the process of rules' refinement, the rules violating the normality are eliminated. The effectiveness of the obtained knowledge base, dependent on the base's size and on rules' reliabilities, is high. The considerations are illustrated with medical examples.