Novosibirsk State University Journal of Information Technologies
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Model-Theoretic Methods of Extraction of Knowledge on the Meaning of Concepts from the Natural Language Texts
Irina Andreevna Korsun, Dmitry Evgenievich Palchunov

Novosibirsk State University
Institute of Mathematics SB RAS

The paper is devoted to the development of model-theoretic methods of knowledge extraction from the natural language texts and, in particular, methods of extraction of concept definitions which are complete relative to the fixed context. The context may be determined by ambient ontology as well as by a set of precedents. The completeness of definitions is considered modulo a special set of "interesting" sentences. The extracted knowledge is represented in the form of fragments of atomic diagrams of algebraic systems. Algorithms of mapping some quantifier-free sentences of predicate logic onto Description Logics (DL) as well as of their further mapping onto OWL are developed and implemented. It gives a possibility to generate new knowledge based on existing knowledge that already contained in the ontology by using automated logical reasoning.

Key Words
domain ontology, model-theoretic methods, fragments of atomic diagrams, concept definitions, knowledge extraction, knowledge generation, automated logical reasoning

How to cite:
Korsun I. A., Palchunov D. E. Model-Theoretic Methods of Extraction of Knowledge on the Meaning of Concepts from the Natural Language Texts // Vestnik NSU Series: Information Technologies. - 2016. - Volume 14, Issue No 3. - P. 34-48. - ISSN 1818-7900. (in Russian).

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Publication information
Main title Vestnik NSU Series: Information Technologies, Volume 14, Issue No 3 (2016).
Parallel title: Novosibirsk State University Journal of Information Technologies Volume 14, Issue No 3 (2016).

Key title: Vestnik Novosibirskogo gosudarstvennogo universiteta. Seriâ: Informacionnye tehnologii
Abbreviated key title: Vestn. Novosib. Gos. Univ., Ser.: Inf. Tehnol.
Variant title: Vestnik NGU. Seriâ: Informacionnye tehnologii

Year of Publication: 2016
ISSN: 1818-7900 (Print), ISSN 2410-0420 (Online)
Publisher: Novosibirsk State University Press
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