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LLM-Powered Natural Language Text Processing for Ontology Enrichment
Artykuł w czasopiśmie
MNiSW
100
Lista 2024
Status: | |
Autorzy: | Mukanova Assel, Miłosz Marek, Dauletkaliyeva Assem , Nazyrova Aizhan, Yelibayeva Gaziza , Kuzin Dmitrii, Kussepova Lazzat |
Dyscypliny: | |
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Rok wydania: | 2024 |
Wersja dokumentu: | Elektroniczna |
Język: | angielski |
Numer czasopisma: | 13 |
Wolumen/Tom: | 14 |
Numer artykułu: | 5860 |
Strony: | 1 - 14 |
Impact Factor: | 2,5 |
Web of Science® Times Cited: | 0 |
Scopus® Cytowania: | 0 |
Bazy: | Web of Science | Scopus |
Efekt badań statutowych | NIE |
Finansowanie: | This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19577922). |
Materiał konferencyjny: | NIE |
Publikacja OA: | TAK |
Licencja: | |
Sposób udostępnienia: | Witryna wydawcy |
Wersja tekstu: | Ostateczna wersja opublikowana |
Czas opublikowania: | W momencie opublikowania |
Data opublikowania w OA: | 4 lipca 2024 |
Abstrakty: | angielski |
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. |