Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Principles of knowledge representation
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Degrees of acyclicity for hypergraphs and relational database schemes
Journal of the ACM (JACM)
Conjunctive-query containment and constraint satisfaction
Journal of Computer and System Sciences - Special issue on the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems
Learning logic programs with structured background knowledge
Artificial Intelligence
The complexity of acyclic conjunctive queries
Journal of the ACM (JACM)
Foundations of Inductive Logic Programming
Foundations of Inductive Logic Programming
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
An Experimental Evaluation of Coevolutive Concept Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
DS '01 Proceedings of the 4th International Conference on Discovery Science
Cyclic pattern kernels for predictive graph mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Labeled graphs provide a natural way of representing objects and the way they are connected. They have various applications in different fields, such as for example in computational chemistry. They can be represented by relational structures and thus stored in relational databases. Acyclic conjunctive queries form a practically relevant fragment of database queries that can be evaluated in polynomial time. We propose a top-down induction algorithm for learning acyclic conjunctive queries from labeled graphs represented by relational structures. The algorithm allows the use of building blocks which depend on the particular application considered. To compensate for the reduced expressive power of the hypothesis language and thus the potential loss in predictive performance, we combine acyclic conjunctive queries with confidence-rated boosting. In the empirical evaluation of the method we show that it leads to excellent prediction accuracy on the domain of mutagenicity.