Semantical considerations on nonmonotonic logic
Artificial Intelligence
All I know: a study in autoepistemic logic
Artificial Intelligence
Truth and modality for knowledge representation
Truth and modality for knowledge representation
A guide to completeness and complexity for modal logics of knowledge and belief
Artificial Intelligence
Computation of extensions of seminormal default theories
Fundamenta Informaticae
BIG: an agent for resource-bounded information gathering and decision making
Artificial Intelligence - Special issue on Intelligent internet systems
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and Data Engineering
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Peculiarity Oriented Multi-database Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Guest Editors' Introduction: Information Enhancement for Data Mining
IEEE Intelligent Systems
Knowledge Discovery in Multiple Databases
Knowledge Discovery in Multiple Databases
Database classification for multi-database mining
Information Systems
Enhancing quality of knowledge synthesized from multi-database mining
Pattern Recognition Letters
Efficient clustering of databases induced by local patterns
Decision Support Systems
Classification algorithm sensitivity to training data with non representative attribute noise
Decision Support Systems
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Rule synthesizing from multiple related databases
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Mining stable patterns in multiple correlated databases
Decision Support Systems
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As the Web has emerged as a large distributed data repository, individuals and organizations have been able to utilize the low-cost information and knowledge on the Internet when making business decisions. Because data in different data sources may be conflictive or untrue, researchers and practitioners must intensify efforts to develop appropriate techniques for its efficient use and management. In this paper, a logical framework is designed for identifying quality knowledge from different data sources, thus working towards the development of an agreed ontology. Our experimental results have demonstrated that the approach is promising, and that a minor data enhancement adjustment could bring higher effectiveness.