Formulating Description Logic learning as an Inductive Logic Programming task
Stasinos Konstantopoulos and Angelos Charalambidis
In: 2010 IEEE Conference on Fuzzy Systems (FUZZ-IEEE 2010), 18-23 Jul 2010, Barcelona, Spain.
We describe an Inductive Logic Programming (ILP) approach to learning descriptions in Description Logics (DL) under uncertainty. The approach is based on implementing many-valued DL proofs as propositionalizations of the elementary DL constructs and then providing this implementation as background predicates for ILP. The proposed methodology is tested on a many-valued variation of eastbound-trains and Iris, two well known and studied Machine Learning datasets.