Lazzat Kussepova
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Lazzat Kussepova has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Aizhan Nazyrova
Institute of Digital Sciences and Artificial Intelligence, L.N. Gumilyov Eurasian National University, Kazakhstan
1 shared publication - Assel Mukanova
Higher School of Information Technology and Engineering, Astana International University, 8 Kabanbay Batyr av., Astana 010000, Kazakhstan
1 shared publication - Assem Dauletkaliyeva
Higher School of Information Technology and Engineering, Astana International University, 8 Kabanbay Batyr av., Astana 010000, Kazakhstan
1 shared publication - Dmitrii Kuzin
Higher School of Information Technology and Engineering, Astana International University, 8 Kabanbay Batyr av., Astana 010000, Kazakhstan
1 shared publication - Gaziza Yelibayeva
L.N. Gumilyov Eurasian National University
1 shared publication - Marek Milosz
Department of Computer Science, Lublin University of Technology
1 shared publication
Latest Publications
- Open Access
- 4 July 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (13) , 5860
LLM-Powered Natural Language Text Processing for Ontology Enrichment
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.
- 4 July 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (13) , 5860
Open AccessLLM-Powered Natural Language Text Processing for Ontology Enrichment
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.