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LLM-Powered Natural Language Text Processing for Ontology Enrichment

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dc.contributor.author Mukanova, Assel
dc.contributor.author Milosz, Marek
dc.contributor.author Dauletkaliyeva, Assem
dc.contributor.author Nazyrova, Aizhan
dc.contributor.author Yelibayeva, Gaziza
dc.contributor.author Kuzin, Dmitrii
dc.contributor.author Kussepova, Lazzat
dc.date.accessioned 2024-09-16T12:27:56Z
dc.date.available 2024-09-16T12:27:56Z
dc.date.issued 2024
dc.identifier.issn 2076-3417
dc.identifier.other doi.org/10.3390/app14135860
dc.identifier.uri http://rep.enu.kz/handle/enu/16426
dc.description.abstract This paper describes a method and technology for processing natural language texts and extracting data from the text that correspond to the semantics of an ontological model. The proposed method is distinguished by the use of a Large Language Model algorithm for text analysis. The extracted data are stored in an intermediate format, after which individuals and properties that reflect the specified semantics are programmatically created in the ontology. The proposed technology is implemented using the example of an ontological model that describes the geographical configuration and administrative–territorial division of Kazakhstan. The proposed method and technology can be applied in any subject areas for which ontological models have been developed. The results of the study can significantly improve the efficiency of using knowledge bases based on semantic networks by converting texts in natural languages into semantically linked data. ru
dc.language.iso en ru
dc.publisher Applied Sciences ru
dc.relation.ispartofseries Volume 14;Issue 13
dc.subject ontology ru
dc.subject Semantic Web ru
dc.subject natural language processing ru
dc.subject ChatGPT ru
dc.subject Large Language Model ru
dc.subject geographic question answering system ru
dc.title LLM-Powered Natural Language Text Processing for Ontology Enrichment ru
dc.type Article ru


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