Machine Learning - special issue on inductive logic programming
Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Logical settings for concept-learning
Artificial Intelligence
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
Predicate Invention in Inductive Data Engineering
ECML '93 Proceedings of the European Conference on Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Strongly Typed Inductive Concept Learning
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Background Knowledge in the Tertius First Order Knowledge Discovery Tool
Background Knowledge in the Tertius First Order Knowledge Discovery Tool
Database dependency discovery: a machine learning approach
AI Communications
Integrating explanatory and descriptive learning in ILP
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Intelligent data analysis
Using Data Mining Techniques to Support the Creation of Competence Ontologies
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
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This paper deals with learning first-order logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised inductive logic programming. First-order logic offers the ability to deal with structured, multi-relational knowledge. Possible applications include first-order knowledge discovery, induction of integrity constraints in databases, multiple predicate learning, and learning mixed theories of predicate definitions and integrity constraints. One of the contributions of our work is a heuristic measure of confirmation, trading off novelty and satisfaction of the rule. The approach has been implemented in the Tertius system. The system performs an optimal best-first search, finding the k most confirmed hypotheses, and includes a non-redundant refinement operator to avoid duplicates in the search. Tertius can be adapted to many different domains by tuning its parameters, and it can deal either with individual-based representations by upgrading propositional representations to first-order, or with general logical rules. We describe a number of experiments demonstrating the feasibility and flexibility of our approach.