20.500.12556/ReVIS-14124 Transforming archival description into semantically enriched form using machine learning Purpose: 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. archival description semantical enrichment machine learning RiC-O ontology KNIME arhivski opis semantična obogatitev strojno učenje RiC-O ontologija KNIME true false false Angleški jezik Slovenski jezik Neznano 2026-07-01 14:59:14 2026-07-01 14:59:14 2026-07-02 03:10:08 0000-00-00 00:00:00 2025 0 0 Str. 45-69 [no.] 1 [Vol.] 35 2025 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 2025-01-01 930.25:004(4) 2670-4560 250077443 https://journal.almamater.si/index.php/atlantiplus/issue/view/51/41 1 a9ce8b2e-754c-11f1-ab13-001a4af901a5 https://revis.openscience.si/Dokument.php?lang=slv&id=17117 Sabadin_Ivancica.pdf Sabadin_Ivancica.pdf 1 6C0B11CC4251FF9E9487E703EB5A6AED bb533dd1a554ab07edbf19009f380a37e98c2d47e86e0cb3e2245835cac0a422 b0ecf38a-754c-11f1-ab13-001a4af901a5 https://revis.openscience.si/Dokument.php?lang=slv&id=17118 Univerza Alma Mater Europaea 0 0 0