Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Formal methods in artificial intelligence
Formal methods in artificial intelligence
Reformulating query plans for multidatabase systems
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Using inductive learning to generate rules for semantic query optimization
Advances in knowledge discovery and data mining
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Inductive Logic Programming: Techniques and Applications
Inductive Logic Programming: Techniques and Applications
Discovering Robust Knowledge from Databases that Change
Data Mining and Knowledge Discovery
Semantic Query Optimization for Tree and Chain Queries
IEEE Transactions on Knowledge and Data Engineering
Learning Transformation Rules for Semantic Query Optimization: A Data-Driven Approach
IEEE Transactions on Knowledge and Data Engineering
Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability
ECML '93 Proceedings of the European Conference on Machine Learning
Query optimization by semantic reasoning
Query optimization by semantic reasoning
A self-organizing database system - a different approach to query optimization
A self-organizing database system - a different approach to query optimization
Discovering Robust Knowledge from Databases that Change
Data Mining and Knowledge Discovery
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
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Many applications of knowledge discovery require the knowledge to be consistent with data. Examples include discovering rules for query optimization, database integration, decision support, etc. However, databases usually change over time and make machine-discovered knowledge inconsistent with data. Useful knowledge should be robust against database changes so that it is unlikely to become inconsistent after database changes. This paper defines this notion of robustness, describes how to estimate the robustness of Horn-clause rules in closed-world databases, and describes how the robustness estimation can be applied in rule discovery systems.