Subject: QA75 Electronic computers. Computer science
Year: 2024
Type: Article
Type: NonPeerReviewed
Title: ONTOLOGY MATCHING AND MANAGEMENT USING MACHINE LEARNING ASPECTS
Author: CANTAŞ, Gökhan
Abstract: Industry 4. zero is a brand-new generation of data era that ambitions to expand expertise bases for tracking developments in enterprise 4. zero. This paper proposes a framework to cope with the improvement of Knowledge Bases for Monitoring Trends in Industry4.zero.In this framework, we suggest an ontological model (COInd4 ontology) for the manufacturing area that describes the assets and strategies within side the manufacturing unit, and sensor observations are analysed via way of means of remark affects the use of contextual data past classical reasoning mechanism for stopping the forecasted undesired sensor effects that effect on from concept. The framework is primarily based totally on an information-pushed technique for Knowledge Graphs (CSV, JSON, and diverse styles of information) technique for growing Machine studying interoperability combining principles from diverse current ontologies for discovered predictive fashions and execution of the fashions. Moreover, LOTHBROK is designed for estimating cardinalities and paying attention to information locality. The assessment confirmed that TAO can gain appreciably quicker question processing overall performance as compared to the nation of the artwork while processing difficult queries in addition to while besides, TAO gives better transparency, flexibility, and cognitive ergonomics than its options Hontology and Accommodation Ontology. It affords a custom-designed approach to abide via way of means of the necessities of the Greek Programme Diavgeia and proposes the identical time approach to encode authorities and administrative decisions/acts that might be universally followed to combine public files produced via way of means of different EU Member States, with positive changes content-wise.
Publisher: Faculty of Natural Sciences and Mathematics, Republic of North Macedonia
Relation: https://eprints.unite.edu.mk/1950/
Identifier: oai:eprints.unite.edu.mk:1950
Identifier: https://eprints.unite.edu.mk/1950/1/revista%20-%202024-219-227.pdfIdentifier: CANTAŞ, Gökhan (2024) ONTOLOGY MATCHING AND MANAGEMENT USING MACHINE LEARNING ASPECTS. JNSM Journal of Natural Sciences and Mathematics of UT, 9 (17-18). pp. 219-227. ISSN 2671-3039
