METHOD OF TERMINOLOGICAL KNOWLEDGE BASES DATA SYNCHRONIZATION
Method for data synchronization in terminological knowledge bases is proposed, based on the use of a neural network and classification by subject area topics. Text data synchronization is one of the key tasks for efficient data processing, which involves systematizing knowledge based on its semantic component and addresses the problem of understanding context based on already known data. To solve this problem, a comprehensive approach is used, which includes a set of solutions and synchronization algorithms at all levels of data processing, including semantic analysis, software and hardware-level algorithms, and the use of optimized data models for specific tasks. During the research, an algorithm for optimal data distribution in terminological knowledge bases was developed based on the semantic value of terms. The main criteria for the distribution of textual data in terminological knowledge bases were identified. The task of updating data in terminological knowledge bases during their population was examined. The problem of conflicting term synchronization based on semantic affiliation to related subject areas was formulated and described. Algorithms for term comparison based on semantic analysis, cosine similarity, the Jaccard method, and the term frequency-inverse document frequency (TF-IDF) method were considered. A description of the synchronization method in accordance with the created models and data structures is provided. The advantages and disadvantages of known methods for text data synchronization using data processing algorithms and methods are described, with examples given of data storage and retrieval tasks. The effectiveness of the synchronization method is demonstrated using examples. Data structures were optimized for efficient storage and retrieval of text data for text classification tasks. A prototype of terminological knowledge bases was created, and the synchronization method was applied using the example of the call center subject area.
Authors
- Andrii Yarovyi (ORCID: https://orcid.org/0000-0002-6668-2425)
- Dmytro Kudriavtsev (ORCID: https://orcid.org/0000-0001-7116-7869)
Institutions
- Sana'a University (YE)
Publication Details
- Published
- 2024-11-21
- DOI
- https://doi.org/10.31649/mccs2024.5-19
- Primary Topic
- Diverse Scientific Research in Ukraine
- Type
- article
- Field-Weighted Citation Impact
- 0.00