Induction of first-order rules and constraints for knowledge discovery
Adaptivity and the ability to make use of knowledge implicitly present in
low-level data are of primary importance for enhancing business and
administrative processes in Europe. With data warehousing in relational
databases becoming more and more popular, there is increasing awareness that
structural or relational learning as investigated in the field of Inductive
Logic Programming (ILP) is a key technique for knowledge discovery.
goals of this project were to enhance existing techniques and develop new ILP
algorithms that can be used as efficient tools for solving hard problems, using
induced first-order classification rules and integrity constraints.
Staff and Students
(Jozef Stefan Institute, Slovenia).
This research was supported by the Royal Society under a
Joint Project with Central and Eastern Europe.