Technologies for extraction, explication and analysis of multimodal contextual knowledge in the study of the formation of the terminological base of the interdisciplinary research direction "Informatization of scientific activity"
Abstract
Digital transformations, which are the basis for the development of modern society, often outstrip the processes of transformation of science. At the same time, they force science to constantly improve both technologies and software tools used in research. In addition, the development of Informatization and digitalization processes leads to the formation of new interdisciplinary scientific directions that reflect the dynamics of social development. Today, there are already established areas of use of information and communication technologies in scientific activities. However, "Informatization of scientific activity" as an integral direction of scientific research has not yet been formed. Therefore, the research is aimed at identifying the main thematic areas that make up the direction of interdisciplinary research "Informatization of scientific activity", determining the core of its terminology base. Based on the application of the approach developed by the authors (synthetic method) to research on the development of topics and the conceptual and terminological landscape of interdisciplinary scientific areas, the main term concepts involved in the formation of the thesaurus are identified. The terminological landscape of the research area is also clarified by identifying the contexts and semantic shades of the terms of the core of the research area.
The study established the multimodality of contextual knowledge used in scientific research. A primary analysis of the representation of non-text modality contexts is performed.
The study was based on data obtained from various digital sources that represent scientific publications over the past 10 years. Voyant-Tools and Sketch Engine systems are used as tools for explication and analysis of contextual knowledge.Full Text:
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Bakanov V.M. Ispol'zovanie sistemy dobrovol'nyh raspredelennyh vychislenij dlja optimizacii jarusno-parallel'noj formy informacionnyh grafov algoritmov // Sovremennoe obrazovanie: soderzhanie, tehnologii, kachestvo. 2015. № 2. P. 129-130.
Borodkin L.I. Istoricheskaja informatika segodnja: vyzovy "cifrovoj jepohi" // Informacionnyj bjulleten' associacii istorija i komp'juter. 2014. №42. P. 3-6.
Volodin A. Digital History: the Craft of Historian in the Digital Age // Istoriya. 2015. Vol. 6. Iss. 8 (41). DOI: 10.18254/S0001228-9-1.
Volodin A.Ju. Cifrovye gumanitarnye nauki (Digital Humanities): vyzovy i tupiki mezhdisciplinarnosti // «Steny i mosty»–IV: mezhdisciplinarnye issledovanija v istorii. M.: Akademicheskij proekt, 2016. P. 139-147.
Garskova I.M. Istoricheskaja informatika: sravnitel'nyj analiz nacional'nyh modelej // Informacionnyj bjulleten' associacii istorija i komp'juter. 2018. №47. P. 25-26.
Gartman T. N., Sovetin F. S. Analiticheskij obzor sovremennyh paketov modelirujushhih programm dlja komp'juternogo modelirovanija himiko-tehnologicheskih sistem // Uspehi v himii i himicheskoj tehnologii. 2012. №11 (140). P. 117-120.
Glinskiy B., Kuchin N., Chernykh I., Orlov Yu., Podkolodnyi N., Likhoshvai V., Kolchanov N. Bioinformatics and High Performance Computing // Program systems: theory and applications. 2015. №4 (27). P. 99-122. DOI: 10.25209/2079-3316-2015-6-4-99-112.
Dolgov V.I., Nelasaja A.V. Metody uvelichenija skorosti kriptograficheskih preobrazovanij na jellipticheskih krivyh // Radiojelektronika. Informatika. Upravlenie. 2004. № 2. P. 72-78.
Drokin I.S., Bukhvalov O.L., Sorokin S.Yu. Sposob formirovaniya matematicheskikh modeley patsienta s ispol'zovaniem tekhnologiy iskusstvennogo intellekta // Patent RU 2 720 363 C2. 29.12.2017
Ezhova N. A., Sokolinsky L. B. Survey of parallel computation models // Computational Mathematics and Software Engineering. — 2019. Vol. 8. №3. P. 58-91. DOI: 10.14529/cmse190304.
Zheltov S. Adaptation factorization problem solution by Sherman–Lehman method to the computing architecture CUDA // History and Archives. 2012. №14 (94). P. 84-91.
Kononova O. V., Lyapin S. Kh., Prokudin D. E. Studying the Interdisciplinary Terminological Landscape of Digital Economy with the Use of Contextual Analysis Tools // International Journal of Open Information Technologies. 2018. Vol. 6. № 12. P. 57-66.
Kononova O. V., Prokudin D. E. An approach to extraction, explication and presentation of contextual knowledge in the study of developing interdisciplinary research areas // International Journal of Open Information Technologies. 2020. Vol. 8, № 1. P. 90-101. URL: http://injoit.org/index.php/j1/article/view/882/844.
Kononova O. V., Prokudin D. E., Smirnova P.V. Approach to Use of Network Scientific Environment for Studying the Interdisciplinary Terminological Landscape of Digital Economy // Information Society: Education, Science, Culture and Technology of Future. Issue 3. P. 53–66. DOI: 10.17586/2587-8557-2019-3-53-66
Koncepcija informatizacii Kazanskogo gosudarstvennogo universiteta. — http://old.kpfu.ru/uit/index.php?id=4&idm=0&num=4
Kruchinin S.V. Mathematical and computer modeling in Political Science and Politics (review) // JSPR. 2017. №4 (42). P. 34-41.
Mozhaeva G.V. Digital Humanities: digital turn in the humanities // Humanitarian Informatics. 2015. № 9. С. 8-23. DOI: 10.17223/23046082/9/1.
Omarov M.D. Analytical review of the methodology of computer modeling // Herald of Dagestan State Technical University. Technical Sciences. 2015. Vol. 36. №1. P. 84-89. DOI: 10.21822/2073-6185-2015-36-1-84-89.
Penkov S.V. Historical information science: history and modernity // Science Almanac. 2019. №12-2 (62). P. 71-73.
Pitulina P. I., Semichevskaya N. P., Solovtsova L. A. Application ofparallel computing in the modern method of cryptanalysis // Scientists notes PNU. 2016. Vol. 7. № 4-1. P. 142-148. URL: http://pnu.edu.ru/media/ejournal/articles-2016/TGU_7_194.pdf.
Smelik N. D., Filchenkov A. A. Multimodal topic model for texts and images utilizing their embeddings // Machine Learning and Data Analysis. 2016. Volume 2. Issue 4. С. 421-441. DOI: 10.21469/22233792.2.4.05
Sorokina Yu. V. Notion of multimodality and issues of multimodal lecture discourse analysis // Philology. Theory & Practice. 2017. № 10. Part 1. P. 168-170.
Spitsina A. M., Orlov Yu. L. et al. Supercomputer analysis of genomics and transcriptomics data revealed by high-throughput DNA sequencing // Program systems: theory and applications. — 2015. №1 (24). P. 157-174. DOI: 10.25209/2079-3316-2015-6-1-157-174.
Terehov A.N., Sepman V.Ju., Kijaev V.I., Komarov S.N. Koncepcija informatizacii sankt-peterburgskogo gosudarstvennogo universiteta. https://www.math.spbu.ru/user/ant/all_articles/068_Terekhov_Kiyaev_ Komarov_Koncept_Informat.pdf.
Cifrovaja gumanitaristika: resursy, metody, issledovanija: materialy Mezhdunar. nauch. konf. (g. Perm, 16–18 may 2017 г.): v 2 ch. / Perm. gos. nac. issled. un-t. Perm, 2017. Ch. 1. 175 p.
Cifrovaja gumanitaristika: resursy, metody, issledovanija: materialy Mezhdunar. nauch. konf. (g. Perm, 16–18 may 2017 г.): v 2 ch. / Perm. gos. nac. issled. un-t. Perm, 2017. Ch. 2. 208 p.
Shamakina A. V. Survey on distributed computing technologies // Computational Mathematics and Software Engineering. 2014. Vol. 3. №3. P. 58-85. DOI: 10.14529/cmse140304.
Yakimets V. N., Kurochkin I. I. Development of voluntary distributed computing projects based on roadmaps and multi-parameter assessments // International Journal of Open Information Technologies. 2020. Vol. 8. № 1. P. 1-8. URL: http://injoit.org/index.php/j1/article/view/864/831.
Yamshchikov O.N. Computer modeling in traumatology and orthopedics (literature review) // Tambov University Reports. Series: Natural and Technical Sciences. 2014. №6. P. 1974-1979. URL: http://journals.tsutmb.ru/go/1810-0198/2014/6/1974-1979.
Bougiatiotis K., Giannakopoulos T. Enhanced movie content similarity based on textual, auditory and visual information // Expert Systems with Applications. 2018. Volume 96. P. 86-102. DOI: 10.1016/j.eswa.2017.11.050
Cromley J. G., Kunze A. J., Parpucu Dane A. Multi-text multi-modal reading processes and comprehension // Learning and Instruction. 2021. Volume 71. 101413. DOI: 10.1016/j.learninstruc.2020.101413
Dancygier B., Vandelanotte L. Image-schematic scaffolding in textual and visual artefacts // Journal of Pragmatics. 2017. Volume 122. P. 91-106. DOI: 10.1016/j.pragma.2017.07.013
Dimitrovski I., Kocev D., Kitanovski I., Loskovska S., Džeroski S.Improved medical image modality classification using a combination of visual and textual features // Computerized Medical Imaging and Graphics. 2015. Volume 39. P. 14-26, DOI: 10.1016/j.compmedimag.2014.06.005
Kress G. What Is Mode? // A Handbook of Multimodal Analysis / ed. by C. Jewitt. L. N. Y.: Routledge, 2009. P. 54-67.
Kumar A., Srinivasan K., Cheng W.-H., Zomaya A.Y. Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data // Information Processing & Management. 2020. Volume 57. Issue 1. 102141. DOI: 10.1016/j.ipm.2019.102141
Wang K., Meng W., Li S., Yang S. Multi-Modal Mention Topic Model for mentionee recommendation // Neurocomputing. 2019. Volume 325. P. 190-199. DOI: 10.1016/j.neucom.2018.10.024
Xu J., Huang F., Zhang X., Wang S., Li C., Li Z., He Y. Visual-textual sentiment classification with bi-directional multi-level attention networks // Knowledge-Based Systems. 2019. Volume 178. P. 61-73. DOI: 10.1016/j.knosys.2019.04.018
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