TY  - JOUR
T1  - Using RDF Models to Create Knowledge Bases in the Kazakh Language: Comparison with Other Methods
AU  - Mukanova, Assel
AU  - Abdıkalyk, Gulnazym
AU  - Nazyrova, Aizhan
AU  - Dauletkalıyeva, Assem
PY  - 2023
DA  - December
DO  - 10.55549/epstem.1412449
JF  - The Eurasia Proceedings of Science Technology Engineering and Mathematics
JO  - EPSTEM
PB  - ISRES Publishing
WT  - DergiPark
SN  - 2602-3199
SP  - 633
EP  - 640
VL  - 26
LA  - en
AB  - Currently, there is a rapid development of information technologies, the amount of information on the Internet is growing very fast and it is becoming increasingly difficult to find the necessary information. A search using keywords does not give results adequate to the meaning of the information sought. Therefore, the creation of a technology for designing intelligent question answering systems in the Kazakh language based on the presentation, processing and extraction of knowledge is a very actual problem, since it is in such a system that the linguistic and semantic relationships between the texts of the request and the answer can be taken into account. This research paper focuses on the integration of the Resource Description Framework (RDF) model, a semantic web technology, and provides a detailed evaluation of data mining techniques in Kazakh. The paper examines many Kazakh language data collection methods such as online scraping, community collaboration and translation. It also explores the function of RDF models in organizing knowledge, connecting data points and adding semantic richness to datasets. The paper discusses linguistic features and challenges unique to the Kazakh language and emphasizes the need to address these challenges with domain-specific data. The need for thorough cleaning, annotation and data quality assurance is emphasized to guarantee the reliability and use of the collected datasets. Within global communications and technology, the study emphasizes the importance of languages other than English and examines how semantic web technologies can improve data representation and knowledge retrieval. The study lays the groundwork for future initiatives to address the shortage of datasets in languages with fewer resources and to create semantic web technologies for language diversity.
KW  - Resource description framework (RDF) model
KW  - Question-Answering system
KW  - Ontology model
KW  - Knowledge base
CR  - Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval (Vol. 463, No. 1999). New York, NY: ACM Press.
CR  - Bekarystankyzy, A., Mamyrbayev, O., Mendes, M., Oralbekova, D., Zhumazhanov, B., & Fazylzhanova, A. (2023). Automatic speech recognition improvement for Kazakh language with enhanced language model. In Asian Conference on Intelligent Information and Database Systems (pp. 538-545). Cham: Springer Nature.
CR  - Bird, S. (2006). NLTK.: The natural language toolkit. In Proceedings of the COLING/ACL on Interactive Presentation Sessions, 69-72.
UR  - https://doi.org/10.55549/epstem.1412449
L1  - https://dergipark.org.tr/en/download/article-file/3630913
ER  -