Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Machine Learning - special issue on inductive logic programming
Object-oriented modeling and design for database applications
Object-oriented modeling and design for database applications
The Unified Modeling Language reference manual
The Unified Modeling Language reference manual
Advances in Inductive Logic Programming
Advances in Inductive Logic Programming
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Multi-relational Decision Tree Induction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
ALT '95 Proceedings of the 6th International Conference on Algorithmic Learning Theory
Relational Knowledge Discovery in Databases
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Literate Modelling - Capturing Business Knowledge with the UML
«UML» '98 Selected papers from the First International Workshop on The Unified Modeling Language «UML»'98: Beyond the Notation
Top-down induction of first-order logical decision trees
Artificial Intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Propositionalisation and Aggregates
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Association discovery from relational data via granular computing
Information Sciences: an International Journal
Granular computing for relational data classification
Journal of Intelligent Information Systems
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Although there is a growing need for multi-relational data mining solutions in KDD, the use of obvious candidates from the field of Inductive Logic Programming (ILP) has been limited. In our view this is mainly due to the variation in ILP engines, especially with respect to input specification, as well as the limited attention for relational database issues. In this paper we describe an approach which uses UML as the common specification language for a large range of ILP engines. Having such a common language will enable a wide range of users, including non-experts, to model problems and apply different engines without any extra effort. The process involves transformation of UML into a language called CDBL, that is then translated to a variety of input formats for different engines.