Skip to main content
Log in

Indicators, Models and Methods for Analysis and Estimation of Structures of Conceptually Connected Texts

  • Intellectual Control Systems, Data Analysis
  • Published:
Automation and Remote Control Aims and scope Submit manuscript

Abstract

This work is devoted to the problem of systematizing the terminology of control theory. For this purpose, we consider elements of the methodology for evaluating the integrity of conceptually connected texts. We demonstrate the results of the use of indicators, models and methods for assessing the integrity of terminology standards for technical diagnostics and give recommendations for improving the terminology of standards.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Teoriya upravleniya. Terminologiya (Control Theory. Terminology), no. 107, Moscow: Nauka, 1988.

  2. Sobolevskii, A.N., Convergence or Integration? http://www.sbras.info/articles/sciencestruct/konvergentsiya-ili-integratsiya (accessed at 23.01.2017).

  3. Novikov, D.A., Systems of Interdisciplinary Nature and Engineering Education, Inzhenern. Pedagogika, 2011, no. 13, pp. 178–185.

    Google Scholar 

  4. Valgina, N.S., Teoriya teksta (Theory of Text), Moscow: Logos, 2003.

    Google Scholar 

  5. Fedorchenko, L.A., Khairova, N.F., Dovnar’, A.I., et al., A Method for Automated Construction of the Semantic Network of a Teaching Discipline’s Terms, Radioelektr. Komp’yut. Sist., 2011, no. 4, pp. 115–120.

    Google Scholar 

  6. Skorokhod’ko, E.F., Semanticheskie seti i avtomaticheskaya obrabotka teksta (Semantic Network and Automated Text Processing), Kiev: Naukova Dumka, 1983.

    MATH  Google Scholar 

  7. Fedorchenko, L.A., Formalized Representation of a Text Fragment from a Study Material, Vestn. Mezhd. Slavyan. Univ., Ser. Tekh. Nauk., 2007, no. 1, pp. 44–52.

    Google Scholar 

  8. Apresyan, Yu.D., Leksicheskaya semantika. Sinonimicheskie sredstva yazyka (Lexical Semantics. Synonymic Means of a Language), Moscow: Nauka, 1974.

    Google Scholar 

  9. Novikov, A.I., Semanticheskie rasstoyaniya v yazyke i tekste (Semantic Distances in Language and Text), Moscow: Nauka, 1990.

    Google Scholar 

  10. Kryukov, K.V., Pankova, L.A., Pronina, V.A., et al., Semantic Proximity Measures in Ontology, Probl. Upravlen., 2010, no. 5, pp. 3–14.

    Google Scholar 

  11. Al-Smadi, M., Jaradat, Z., Al-Ayyoub, M., and Jararweh, Y., Paraphrase Identification and Semantic Text Similarity Analysis in Arabic News Tweets Using Lexical, Syntactic, and Semantic Features, Inform. Process. Manage., 2017, vol. 53, no. 3, pp. 640–652.

    Article  Google Scholar 

  12. Maia, C., Cesar, P., Vasconcellos, S.M., and Teles, C.M., Terminology Applied to Scientific Literature on Environmental Management: Guidelines for a Micro-Thesaurus Building, Perspectivas em ciencia da informacao, 2017, vol. 22, no. 1, pp. 80–99.

    Article  Google Scholar 

  13. Montero, J., Bustince, H., Franco, C., Rodriguez, J.T., Gomez, D., Pagola, M., Fernandez, J., and Barrenechea, E., Paired Structures in Knowledge Representation, Knowledge-Based Syst., 2016, vol. 100, pp. 50–58.

    Article  Google Scholar 

  14. Lukashevich, N.V. and Chetverkin, I.I., Combining Thesaurus and Corpus Knowledge to Extract Sentiment Words, Sist. Sredstva Informatiki, 2015, vol. 25, no. 1, pp. 20–33.

    Google Scholar 

  15. Ferreira, R., Lins, R.D., Simske, S.J., Freitas, F., and Riss, M., Assessing Sentence Similarity through Lexical, Syntactic and Semantic Analysis, Comput. Speech Language, 2016, vol. 39, pp. 1–28.

    Article  Google Scholar 

  16. Lange, J.-M., Discover and Use Real-World Terminology with IBM Watson Content Analytics: Build Sample Domain Dictionaries for Data Analysis, IBM developerWorks, 2014.

    Google Scholar 

  17. Martinez-Gil, J., CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures, Cognitive Syst. Res., 2016, vol. 40, pp. 8–17.

    Article  Google Scholar 

  18. Dobrov, B.V. and Lukashevich, N.V., Linguistic Ontology in Natural Sciences and Techologies for Applications in Information Retrieval, Uch. Zap. Kazan Gos. Univ., Ser. Fiz.-Mat. Nauk., 2007, vol. 149, part 2, pp. 49–72.

    Google Scholar 

  19. Vlachidis, A. and Tudhope, D., A Knowledge-Based Approach to Information Extraction for Semantic Interoperability in the Archaeology Domain, J. Associat. Informat. Sci. Technol., 2016, vol. 67, no. 5, pp. 1138–1152.

    Article  Google Scholar 

  20. Gubanov, D.A., Makarenko, A.V., and Novikov, D.A., Methods for Analyzing the Terminological Structure of a Domain (with the Example of Methodology), Upravlen. Bol’shimi Sist., 2013, no. 43, pp. 5–33.

    Google Scholar 

  21. Nokel’, M.A. and Lukashevich, N.V., Topic Models: Adding Bigrams and Accounting for the Similarity Between Unigrams and Bigrams, Vychisl. Met. Programmir., 2015, vol. 16, no. 2, pp. 215–234.

    Google Scholar 

  22. Naumov, I.S. and Vykhovanets, V.S., Estimating the Complexity and Hardness of Teaching Tasks Based on Syntactic Analysis of Texts, Upravlen. Bol’shimi Sist., 2014, no. 48, pp. 97–131.

    Google Scholar 

  23. Gutierrez, Y., Vazquez, S., and Montoyo, A., A Semantic Framework for Textual Data Enrichment, Expert Syst. Appl., 2016, vol. 57, pp. 248–269.

    Article  Google Scholar 

  24. Anisimov, A.V., Liman, K.S., and Marchenko, A.A., Methods for Computing Measures of Semantic Similarity between Words in a Natural Language. http://lingvoworks.org.ua(accessed at 09.04.2013).

  25. Leacock, C. and Chodorow, M., Combining Local Context and WordNet Similarity for Word Sense Identification, in WordNet: An Electronic Lexical Database, Fellbaum, C., Ed., Boston: MIT Press, 1998, pp. 265–283.

    Google Scholar 

  26. Patwardhan, S., Banerjee, S., and Pedersen, T., Using Measures of Semantic Relatedness for Word Sense Disambiguation, Proc. 4 Int. Int. Conf. on Intelligent Text Processing and Computational Linguistics, 2003, pp. 241–257.

    Chapter  Google Scholar 

  27. Berners-Lee, T.J., Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential, Boston: MIT Press, 2005.

    Google Scholar 

  28. Sistemnyi analiz i prinyatie reshenii: slovar’-spravochnik (Systems Analysis and Decision Making: Reference), Volkova, V.N. and Kozlov, V.N., Eds., Moscow: Vysshaya Shkola, 2004.

    Google Scholar 

  29. Filosofskii entsiklopedicheskii slovar’ (Philosophical Encyclopaedic Dictionary), Gubskii, E.F. et al., Eds., Moscow: INFRA-M, 2009.

    Google Scholar 

  30. Novaya filosofskaya entsiklopediya (New Philosophical Encyclopaedia), in 4 vols., Inst. of Philosophy of the RAS, National Socio-Scientific Foundation, Moscow: Mysl’, 2010, vol. 4

  31. Korotets, I.D., Politologiya. Slovar’ (Politology. A Dictionary), Konovalov, V.N., Ed., Moscow: RGU, 2010.

  32. Ozhegov, S.I. and Shvedova, N.Yu., Tolkovyi slovar’ russkogo yazyka: 80 000 slov i frazeologicheskikh vyrazhenii (Russian Language Dictionary: 80 000 Words and Idioms), Moscow: OOO “A TEMP,” 2006, 4th ed.

    Google Scholar 

  33. Entsiklopediya sotsiologii (Encyclopaedia of Sociology), 2009. http://dic.academic.ru/dic.nsf/socio/4565/(accessed at 20.09.2016).

  34. Filosofskii entsiklopedicheskii slovar’ (Philosophical Encyclopaedic Dictionary), Il’ichev, L.F., Fedoseev, P.N., Kovalev, S.M., and Panov, V.G., Eds., Moscow: Sovetskaya Entsiklopediya, 1983.

    Google Scholar 

  35. Russian Governmental Standard ISO/TS 18308–2008: Informatizatsiya zdorov’ya. Trebovaniya k arkhitekture elektronnogo ucheta zdorov’ya (Informatization of Health. Requirements to an e-Health Architecture).

  36. Russian Governmental Standard ISO/MEK TO10032–2007: Etalonnaya model’ upravleniya dannymi (Reference Data Management Model).

  37. Russian Governmental Standard 34.321–96: Informatsionnye tekhnologii. Sistema standartov po bazam dannykh. Etalonnaya model’ upravleniya dannymi (Information Technologies. System of Database Standards. Reference Data Management Model).

  38. ISO 2382–8:1998: Sistemy obrabotki informatsii. Slovar’. Ch. 8. Upravlenie, tselostnost’ i zashchita (Information Processing Systems. Reference. Part 8. Control, Integrity, and Security).

  39. Pavlovskii, I.S., Evaluating the Integrity of Uniform Functional Profiles of Control Systems for Technological Processes, Proc. 16th Intl. Conf. “Systems for Design, Technological Preparation of Production and Control over the Lifecycle Stages of an Industrial Product” (CAD/CAM/PDM-2016), Moscow: OOO “Analitik,” 2016, pp. 49–53.

    Google Scholar 

  40. Voishvillo, E.K., Ponyatie kak forma myshleniya: logiko-gnoseologicheskii analiz (Notion as a Form of Reasoning: A Logical and Epistemological Analysis), Moscow: LKI, 2007, 2nd ed.

    Google Scholar 

  41. Berge, C., Théorie des graphes et ses applications, Paris: Dunod, 1958. Translated under the title Teoriya grafov i ee primeneniya, Moscow: Inostrannaya Literatura, 1962.

    Google Scholar 

  42. Pavlovskii, I.S., Hierarchical Structurization of a Semantic Network of Terms in Solving the Problem of Meaningful Integration of Large Information Resources, Proc. 9th Intl. Conf. “Control over Development of Large-Scale Systems” (MLSD’2016), Moscow: IPU RAN, 2016, vol. 2, pp. 433–436.

    Google Scholar 

  43. Pavlovskii, I.S., Hierarchical Structurization as a Method of Finding Contradictions in Control over Complex Systems, Proc. XXIII Intl. Conf. Problems of Security Control for Complex Systems, Moscow, December 2015, Arkhipova, I.I. and Kul’ba, V.V., Eds., Moscow: RGGU, 2015, pp. 7–51.

    Google Scholar 

  44. Pavlovskii, I.S., Method of Extracting the Fragments of a Uniform Binary Network in the Problem of Evaluating the Integrity of a Complex System, Proc. XXIV Intl. Conf. “Control Problems for Security of Complex Systems,” Moscow, December 2016, Arkhipova, N.I. and Kul’ba, V.V., Eds., Moscow: RGGU, 2016, pp. 302–305.

    Google Scholar 

  45. Melikhov, A.N., Bershtein, L.S., and Korovin, S.Ya., Situatsionnye sovetuyushchie sistemy s nechetkoi logikoi (Situational Advisory Systems with Fuzzy Logic), Moscow: Nauka, 1990.

    Google Scholar 

  46. Volkova, V.N. and Denisov, A.A., Osnovy teorii sistem i sistemnogo analiza (Fundamentals of the Theory of Systems and Systems Analysis), St. Petersburg: S.-Peterburg. Gos. Tekh. Univ., 1997.

    Google Scholar 

  47. Russian Governmental Standard 20911–75: Tekhnicheskaya diagnostika. Osnovnye terminy i opredeleniya (Technical Diagnostics. Basic Terms and Definitions).

  48. Russian Governmental Standard 20911–89: Mezhdunarodnyi standart. Tekhnicheskaya diagnostika. Terminy i opredeleniya (International Standard. Technical Diagnostics. Terms and Definitions).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to I. S. Pavlovskii.

Additional information

Original Russian Text © I.S. Pavlovskii, P.P. Parkhomenko, 2018, published in Avtomatika i Telemekhanika, 2018, No. 9, pp. 106–121.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pavlovskii, I.S., Parkhomenko, P.P. Indicators, Models and Methods for Analysis and Estimation of Structures of Conceptually Connected Texts. Autom Remote Control 79, 1630–1642 (2018). https://doi.org/10.1134/S0005117918090084

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0005117918090084

Keywords

Navigation