Forest Mixing: investigating the impact of multiple search trees and a shared refinements pool on ontology learning
Marco Pop-Mihali; Adrian Groza · 2023 · arXiv
WASTE classifies this as Failed Experiment Report · AI classification, approximate
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Abstract (excerpt)
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 streamlin
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Metadata source: arXiv