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
References
1. Golenkov V. V., Guliakina N. A. (2014) Project of Open Semantic Technology of the Componental Design of Intelligent Systems. Part 1. The Principles of Creation. Ontology Designing. (4), 42–64 (in Russian).
2. Fader A., Soderland S., Etzioni O. (2011) Identifying Relations for Open Information Extraction. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Edinburgh, Scotland, UK. 1535–1545.
3. Etzioni O., Fader A., Christensen J., Soderland S., Mausam M. (2011) Open Information Extraction: the Second Generation. International Joint Conference on Artificial Intelligence (IJCAI'11). Barcelona, AAAI Press. Publ. 3–10.
4. Tseng Y. H., Lee L. H. (2014) Chinese Open Relation Extraction for Knowledge Acquisition. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, April 26–30. 12–16.
5. Davydenko I. T. (2017) Ontology-Based Knowledge Base Design. Open Semantic Technologies for Intelligent Systems (OSTIS–2017): Materials of the International Scientific and Technical Conference, Minsk, 16–18 Feb. 2017. Minsk, Belarusian State University of Informatics and Radioelectronics. 57–72.
6. Qian Longwei (2020) Ontological Approach to Chinese Text Processing. Doklady BGUIR. 18 (6), 49–56. http://dx.doi.org/10.35596/1729-7648-2020-18-6-49-56 (in Russian).
7. Shunkevich D. V. (2017) Ontology-Based Design of Knowledge Processing Machines. Open Semantic Technologies for Intelligent Systems (OSTIS–2017): Materials of the International Scientific and Technical Conference, Minsk, 16–18 Feb. 2017. Minsk, Belarusian State University of Informatics and Radioelectronics. 73–94.
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
Views: 521
Email this article 