Transforming archival description into semantically enriched form using machine learningSabadin, Ivančica (Avtor) archival descriptionsemantical enrichmentmachine learningRiC-O ontologyKNIMEPurpose: The purpose of this paper is to determine if it is possible to transform the archival description in the relational database into an ontology with a ma-chine learning algorithm. Method/approach: The research will be based on the CRISP-ML(Q) method. The following steps will be carried out: Business and data understanding; Data preparation; Modelling and Evaluation.Results: After the transformation of the archival description, the Random Forest classification was used to predict the predicate in the semantic triplets. The re-sults obtained were: precision: 86.1% and accuracy: 96.5%.Conclusions / findings: Based on the results, we can conclude that the hypoth-esis was confirmed and that the machine learning algorithms are suitable for transforming the archival description in a structured form into an ontology.20252026-07-01 14:59:14Neznano14124sl