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arXiv · 2309.17252

Forest Mixing: investigating the impact of multiple search trees and a shared refinements pool on ontology learning

Marco Pop-Mihali·Adrian Groza
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Abstract

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.

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Marco Pop-Mihali, Adrian Groza. 2023-09-29. Forest Mixing: investigating the impact of multiple search trees and a shared refinements pool on ontology learning. https://arxiv.org/abs/2309.17252

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