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.

翻译：我们致力于开发白盒机器学习算法，重点关注描述逻辑中公理的学习算法。本文对DL-Learner工具中包含的类表达式学习本体工程（CELOE）算法进行了扩展。该方法采用多棵搜索树和共享的精炼池，将搜索空间划分为多个更小的子空间。我们引入了每棵树中最佳类表达式的合取运算，保留提供最多信息的结果，旨在促进从多样化的起始类出发进行探索，并简化本体中类表达式的查找过程（尤其适用于大规模搜索空间）。当前实现与设置表明，森林混合方法并未优于传统CELOE方法。尽管如此，该方法提出的概念性方案可能为未来改进本体中类表达式查找技术提供启示。