  We aim at development white-box machine learning algorithms. We focus here on
algorithms for learning axioms in description logic. We extend the Class
Expression Learning for Ontology Engineering (CELOE) algorithm contained in the
DL-Learner tool. The approach uses multiple search trees and a shared pool of
refinements in order to split the search space in smaller subspaces. We
introduce the conjunction operation of best class expressions from each tree,
keeping the results which give the most information. The aim is to foster
exploration from a diverse set of starting classes and to streamline the
process of finding class expressions in ontologies. %, particularly in large
search spaces. The current implementation and settings indicated that the
Forest Mixing approach did not outperform the traditional CELOE. Despite these
results, the conceptual proposal brought forward by this approach may stimulate
future improvements in class expression finding in ontologies. % and influence.
% the way we traverse search spaces in general.

Artificial Intelligence