Extracting Scientific and Technical Facts from Industry Documents Based on Methods of Their Semantic-syntactic and Conceptual Analysis

 
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Abstract

Extraction of scientific and technical facts is a difficult task in terms of correctness of the obtained information. The proposed fact extraction model is based on clear ideas about the semantic structure of the text, expressed as a hierarchy of syntactic constructions of meaning units, which allows identifying interphrase relations in contacted sentences. Individual words, word combinations inherent to a particular subject area and forming its conceptual composition are used as meaning units. The procedures of phraseological, conceptual and semantic-syntactic analysis of texts are used to process the source text.

General Information

Keywords: fact extraction, semantic-syntactic analysis, semantic-syntactic analysis, conceptual analysis, semantic triad

Journal rubric: Data Analysis

OpenAlex citations: 0

OpenAlex trends: Engineering Diagnostics and Reliability, Advanced Data Processing Techniques, Advanced Computational Techniques and Applications

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Number of citations: 0

Topics

Engineering Diagnostics and Reliability

This cluster of papers focuses on materials engineering in industrial applications, particularly in the fields of corrosion, steel strength, pipeline durability, deformation processes, and industrial safety. It addresses issues such as fatigue, electrochemical parameters, friction welding, and heat resistance in various mechanical components and equipment used in oil and gas, power generation, and other industrial sectors.

Number of works: 66124  |  Total number of citations: 120610

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Advanced Data Processing Techniques

This cluster of papers focuses on the modeling and control of multidimensional systems, with an emphasis on redundant transmission, fuzzy controller adaptation, fault tolerance, real-time systems, and energy efficiency in various domains such as cyber-physical systems, network traffic analysis, and industrial automation.

Number of works: 60256  |  Total number of citations: 206829

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Advanced Computational Techniques and Applications

This cluster of papers covers a wide range of topics related to artificial intelligence, expert systems, machine learning, control systems, fault diagnosis, ontology, and spatial information. It explores applications such as wavelet analysis, bioenergy, small business strategies, and multidisciplinary design optimization. The papers also delve into topics like language, logic, fluid mechanics, and marine data services.

Number of works: 180645  |  Total number of citations: 205000

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Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2024140102

Received 04.03.2024

Accepted

Published

For citation: Kan, A.V., Kozlovskaya, Y.D., Tokolova, A.A. (2024). Extracting Scientific and Technical Facts from Industry Documents Based on Methods of Their Semantic-syntactic and Conceptual Analysis. Modelling and Data Analysis, 14(1), 27–40. (In Russ.). https://doi.org/10.17759/mda.2024140102

© Kan A.V., Kozlovskaya Y.D., Tokolova A.A., 2024

License: CC BY-NC 4.0

References

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Information About the Authors

Anna V. Kan, Candidate of Science (Engineering), Associate Professor, Institute of Moscow Aviation Institute (National Research University), Head of the Analytical Department, Federal State Budgetary Institution «National Research Center» Institute named after N.E. Zhukovsky, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-9410-406X, e-mail: kan_a@mail.ru

Yana D. Kozlovskaya, Student, Institute of Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-1780-5687, e-mail: yana_kozlovskaia@mail.ru

Alina A. Tokolova, master's student , Institute of Computer Science and Applied Mathematics, Moscow Aviation Institute (National Research University) (MAI), Moscow, Russian Federation, e-mail: tokolovaa@gmail.com

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