nach oben

Automatic Documentation and Mathematical Linguistics

Erschienen in:

01.09.2020 | INFORMATION SYSTEMS

A Methodology of Constructing a Distributed Information System for Searching for Scientific and Technical Information Based on an Object Data Model

verfasst von: V. N. Shvedenko, O. V. Shchekochikhin, Y. A. Sinkevich

Erschienen in: Automatic Documentation and Mathematical Linguistics | Ausgabe 5/2020

Einloggen, um Zugang zu erhalten

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

A methodology of organizing the search for scientific and technical information and the creation of an appropriate information retrieval system is considered. The methodological basis is the three-level hierarchical structure of ontologies. The first level forms the conceptual apparatus of the subject area, the second level details the terminology of a specific area of scientific knowledge. Ontologies of the third level are focused on a specific scientific direction, they allow one to structure objects and research methods, as well as the scientific result and practical value of a scientific publication. An object data model is used. The basics of the language of information queries are proposed.

Vorheriger Artikel Analogies between Physics and Information Processing

Nächster Artikel Analytical Models of Parrying Negative External Influences on a Network Information System

Date of introduction 1997-07-01. Developed by the All-Russian Institute of Scientific and Technical Information with the participation of the Russian State Library and TC 191 “Scientific and Technical Information, Library and Publishing”, introduced by the State Standard of Russia, adopted by the Interstate Council for Standardization, Metrology and Certification (Protocol No. 9 dated April 12, 1996.)

Manning, C., Raghavan, P., and Schutze, H., Introduction to Information Retrieval, New York: Cambridge Univ. Press, 2008.CrossRef

Chi, T., Tezuka, T., Oyama, S., Tajima, K., and Tanaka, K., Web search improvement based on proximity and density of miltiple keywords, in Data Engineering Workshops, Edinburgh, 2006. https://doi.org/10.1109/ICDEW.2006.164

Shen, D. and Lapata, M., Using semantic roles to improve question answering, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 2007. http://www.aclweb.org/anthology/D/D07/D07-1002. Accessed March 23, 2020.

Stenchikova, S., Hakkani-Tur, D., and Tur, G., QASR: Spoken question answering using semantic role labeling, ASRU-2005, 9th Biannual IEEE Workshop on Automatic Speech Recognition and Understanding, Edinburgh, 2005.

Hwang, M., Kong, H., Baek, S., and Kim, P., TSM: Topic selection method of web documents, Modelling & Simulation, 2007. AMS '07. First Asia International Conference, Edinburgh, 2007, pp. 369–374.

Lewandowski, D., Query types and search topics of German Web search engine users, Inf. Serv. Use, 2006, vol. 26, no. 4, pp. 261–269.CrossRef

Wang, Y., Li, H., Wang, H., and Zhu, K.Q., Toward topic search on the Web, Proceedings of ER, Beijing, 2012.

Cheng, T., Lauw, H.W., and Paparizos, S., Entity synonyms for structured Web search, IEEE Trans. Knowl. Data Eng., 2012, vol. 24, no. 10, pp. 1862–1875. https://doi.org/10.1109/TKDE.2011.168CrossRef

Wei, X., Peng, F., Tseng, H., Lu, Y., Wang, X., and Dumoulin, B., Search with synonyms: Problems and solutions, International Conference on Computational Linguistics, Singapore, 2010, pp. 1318–1326.

10.

Paananen, A., Comparative analysis of Yandex and Google search engines, Master’s Thesis, Metropolia Ammattikorkeakoulu, 2012. http://publications.theseus.fi/handle/10024/46483. Accessed March 23, 2013.

11.

Sinkevich, E.A., The process of information processing by search programs in the Internet, Inf.-Ekon. Aspekty Stand. Tekh. Regul., 2018, vol. 6, no. 46, pp. 30–36.

12.

Suvorov, R.E. and Sochenkov, I.V., Establishing the similarity of scientific and technical documents based on thematic significance, Sci. Tech. Inf. Process., 2015, vol. 42, pp. 321–327.CrossRef

13.

Tikhomirov, I.A., Question-answer search in the intellectual search system Exactus, Trudy chetvertogo rossiiskogo seminara po otsenke metodov informatsionnogo poiska ROMIP’2006 (Proceedings of the Fourth Russian Seminar on the Evaluation of Information Retrieval Methods ROMIP 2006), St. Petersburg: NU TsSI, 2006, pp. 80–85.

14.

Komarov, A.Yu., Ontologies on graphs, in Epistemy: Sb. nauchnykh statei (Epistems: Collection of Scientific Articles), vol. 12: Intellektual’nye sistemy: Ontologiya, epistemologiya i prakseologiya (Intelligent Systems: Ontology, Epistemology, and Praxeology), Yekaterinburg: Delovaya Kn., 2017, pp. 16–22.

15.

Shchekochikhin, O.V., Shvedenko, V.V., and Shvedenko, P.V., Ontology of concepts of the information system with behavior, Nauchno-Tekh. Vestn. Povolzh’ya, 2016, no. 5, pp. 223–226.

16.

Maedche, A. and Staab, S., Semi-automatic engineering of ontologies from text, Proceedings of the 12th International Conference on Software Engineering and Knowledge Engineering, Berlin–Heidelberg, 2000, pp. 231–239.

17.

Maedche, A. and Staab, S., Ontology learning for the semantic web, IEEE Intell. Syst., 2001, vol. 16, no. 2, pp. 72–79. https://doi.org/10.1109/5254.920602CrossRef

18.

Navigli, R., Velardi, P., and Gangemi, A., Ontology learning and its application to automated terminology translation, IEEE Intell. Syst., 2003, vol. 18, no. 1, pp. 22–31. https://doi.org/10.1109/MIS.2003.1179190CrossRef

19.

Shvedenko, V.N., Shvedenko, V.V., and Shchekochikhin, O.V., Using structural polymorphism in creating process-based management information systems, Autom. Doc. Math. Linguist., 2018, vol. 52, no. 6, pp. 81–84.CrossRef

20.

Shvedenko, V.N., Shvedenko, V.V., and Shchekochikhin, O.V., Using structural polymorphism in creating process-based management information systems, A-utom. Doc. Math. Linguist., 2018, vol. 52, no. 6, pp. 81–84.

21.

Shvedenko, V.N., Shchekochikhin, O.V., and Shvedenko, V.V., Theoretical foundations for creating digital twins using polymorphism methods, Inf.-Ekon. Aspekty Stand. Tekh. Regul., 2019, no. 1. http://iea.gostinfo.ru/ magazine_2019_01(47).html.

22.

Kleshchev, A.S. and Artem’eva, I.L., Mathematical models of domain ontologies. Part 1. Existing approaches to the definition of the concept of “ontology,” Nauchno-Tekh. Inf., Ser. 2, 2001, no. 2, pp. 20–27.

23.

Antonov, I.V. and Voronov, M.V., A method for automated construction of the domain ontology, Model. Anal. Dannykh, 2011, no. 1, pp. 116–130.

24.

Lee, C.S., Kao, Y.F., Kuo, Y.H., and Wang, M.H., Automated ontology construction for unstructured text documents, Data Knowl. Eng., 2007, vol. 60, no. 3, pp. 547–566. https://doi.org/10.1016/j.datak.2006.04.001CrossRef

25.

Ashino, T., Materials ontology: An infrastructure for exchanging materials information and knowledge, DataScience J., 2010, vol. 9, no. 1, pp. 54–61. https://doi.org/10.2481/dsj.008-041CrossRef

26.

Weng, S., Tsai, H., Liu, S., and Hsu, C., Ontology construction for information classification, Expert Syst. App-l., 2006, vol. 31, no. 1, pp. 1–12. https://doi.org/10.1016/j.eswa.2005.09.007CrossRef

27.

Choi, S.P. and Myaeng, S.H., Terminological paraphrase extraction from scientific literature based on predicate argument tuples, J. Inf. Sci., 2012, vol. 38, no. 6, pp. 593–611.CrossRef

28.

Zhang, C. and Wu, D., Bilingual terminology extraction using multi-level termhood, Electron. Libr., 2012, vol. 30, no. 2, pp. 295–309.CrossRef

29.

Marciniak, M. and Mykowiecka, A., Terminology extraction from medical texts in Polish, J. Biomed Semantics, 2014, no. 5, pp. 1–14. https://doi.org/10.1186/2041-1480-5-24

30.

Macken, L., Lefever, E., and Hoste, V., Texsis: Bilingual terminology extraction from parallel corpora using chunk-based alignment, Terminology, 2013, vol. 19, no. 1, pp. 1–30. https://doi.org/10.1075/term.19.1.01macCrossRef

31.

Sellami, Z. and Camps, V., DYNAMO-MAS: A multi-agent system for building and evolving ontologies from text, in Advances on Practical Applications of Agents and Multi-Agent Systems, Berlin–Heidelberg: Springer, 2012. https://doi.org/10.1007/978-3-642-28786-2_38.

32.

Shvedenko, V.N., Shchekochikhin, O.V., and Cherkasova, N.V., Search for an architectural solution for information support of a digital twin of a complex system, Nauchno-Tekh. Inf., Ser. 2, 2020, no. 4, pp. 18–21.

Titel: A Methodology of Constructing a Distributed Information System for Searching for Scientific and Technical Information Based on an Object Data Model
verfasst von: V. N. Shvedenko
O. V. Shchekochikhin
Y. A. Sinkevich
Publikationsdatum: 01.09.2020
Verlag: Pleiades Publishing
Erschienen in: Automatic Documentation and Mathematical Linguistics / Ausgabe 5/2020
Print ISSN: 0005-1055
Elektronische ISSN: 1934-8371
DOI: https://doi.org/10.3103/S0005105520050039

Springer Professional

A Methodology of Constructing a Distributed Information System for Searching for Scientific and Technical Information Based on an Object Data Model

Abstract

Premium Partner

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Weitere Artikel der Ausgabe 5/2020

On the Methods of Artificial Intelligence for Analysis of Oncological Data

The JSM-System of Psychological and Handwriting Research on Signatures

A Methodology of Using a Concordancer and Table Processor for Authorship Attribution

Analogies between Physics and Information Processing

Analytical Models of Parrying Negative External Influences on a Network Information System

Premium Partner