Ontology-Based Knowledge Acquisition Method for Natural Language Texts
https://doi.org/10.35596/1729-7648-2023-29-1-57-63
Abstract
The main task of knowledge acquisition (also named knowledge extraction) from natural language texts is to extract knowledge from natural language texts into fragment of knowledge base of intelligent system. Through the induction of the related literature about knowledge acquisition at a home country and abroad, this paper analyses the strengths and weaknesses of the classical approach. After emphatically researching the rulebased knowledge extraction technology and the method of building ontology of linguistics, this article proposes a solution to the implementation of knowledge acquisition based on the OSTIS technology. The main feature of this solution is to construct a unified semantic model that is able to utilize ontologies of linguistics (mainly, syntactic and semantic aspect) and integrate various problem-solving models (e. g., rule-based models, neural network models) for solving knowledge extraction process from natural language texts.
About the Author
Longwei Qian
Belarusian
State University of Informatics and Radioelectronics
Belarus
Belarus
Longwei Qian, Postgraduate at the Department
ofIntelligent Information Technologies
220013, Minsk, P. Brovki St., 6
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Review
For citations:
Qian L. Ontology-Based Knowledge Acquisition Method for Natural Language Texts. Digital Transformation. 2023;29(1):57-63. (In Russ.) https://doi.org/10.35596/1729-7648-2023-29-1-57-63
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