V. V. Lytvyn, V. A. Vysotska, M. H. Hrendus


Context. Nowadays there is a constantly increasing interest to the application of the intelligent systems (IS) in different areas
such as information technologies (IT), engineering, medicine, biology, ecology, geography, jurisprudence etc. At the heart of
architecture of modern IS’s knowledge bases (KB) are used, which are formed due to the subject area (SA), where the given IS is
used. The main part of KB is ontology as clearly structured SA’s model, systematic set of terms, which explain the connections
between objects of this SA. Ontologies are generally accepted and widely used in different branches of science such as knowledge
engineering, presentation of knowledge, information search, knowledge management, database design, information modeling and
object-oriented analysis. In particular, Gather company in their researches of IT-market attributed the use of taxonomy/ontology in
his area. Consequently, research of syntactic ontological structures of KB, construction and research of optimal algorithm for syntactic
analysis of Ukrainian language texts and the development of software-algorithmic means of content, automatic referencing of
texts, gathering knowledge, translation etc. are relevant.
Objective. The goal of the work develop a software system for formalizing the rules of syntax of the Ukrainian language in the
form of an ontological basis of knowledge for the purpose of its use for working out natural language texts in the Ukrainian language.
Method. Methods of solving the problem of creating a consolidated resource based on ontological KB were chosen decision
trees, IDEF5 methodology and ontology construction methodology. The results of syntactic analysis work are taken into account by
associative-semantic context analysis to optimize the process of constructing associative context relationships between words and
sentence combinations within the hierarchical network of ontological BB.
Results. A consolidated information resource is created – an ontological KB of parsing analysis of Ukrainian-language text
documents with the help of Protégé 3.4.7.
Conclusions. The method of data extraction based on ontological BZ and FPGA language is developed for the further development
of a consolidated information resource for the syntactic elaboration of text documents. As a result, an ontological type of KB
with FPSM was created. The syntactic structure of the input sentence is the foundation and frame for the next, not less important step
– semantic analysis. This ontological KB of the consolidated LR of syntactic elaboration of Ukrainian-language text documents
serves as a powerful basis for further development of an automated IS for parsing Ukrainian-language texts


analysis; content-analysis; ontology; content management system


Lytvyn Vasyl, Vysotska Victoria, Chyrun Lyubomyr, Dosyn

Dmytro Methods based on ontologies for information resources

processing. Saarbrücken, LAP, 2016, 324 p.

Lytvyn V. V., Vysotska V. A., Dosyn D. H. Metody ta zasoby

opratsyuvannya informatsiynykh resursiv na osnovi ontolohiy.

Lviv, LA «Piramida», 2016, 404 p.

Gruber T. A translation approach to portable ontologies specifications, Knowledge Acquisition, 1993, Vol. 5 (2), pp. 199–220.

Gruber T. Toward Principles for the Design of Ontologies Used for Knowledge Sharing, International Journal Human-

Computer Studies, 1995, Vol. 43(5–6), pp. 907–928.

Guarino N. Formal Ontology, Conceptual Analysis and Knowledge Representation, International Journal of Human-Computer Studies, 1995, Vol. 43(5–6), pp. 625–640.

Sowa J. Conceptual Graphs as a universal knowledge representation, Semantic Networks in Artificial Intelligence, 1992, Vol. 23 (2–5), pp. 75–95.

Bulskov H. Knappe R., Andreasen R. On Querying Ontologies and Databases / H. Bulskov, // Flexible Query Answering Systems, 2004, pp. 191–202.

Callı A., Gottlob G., Pieris A. Advanced processing for ontological queries, Very Large Databases : 36th International Conference, Singapore, September 13–17, 2010 : proceedings. Singapore, VLDB Endowment, 2010, Vol. 3, No. 1, pp. 554–565.

Galopin A., Bouaud J., Pereira S., Seroussi B. An Ontology-

Based Clinical Decision Support System for the Management of

Patients with Multiple Chronic Disorders, Studies in health

technology and informatics, IMIA and IOS Press, 2015,

pp. 275–279.

Zhao Tian An Ontology-Based Decision Support System for

Interventions based on Monitoring Medical Conditions on Patients in Hospital Wards, Master Thesis in Information and

Communication Technology IKT590, Spring. Grimstad, University of Agder, 2014, 125 p.

Ugon A., Sedki K., Kotti A., Seroussi B., Philippe C., Ganascia JG., Garda P., Bouaud J., Pinna A. Decision System Integrating Preferences

to Support Sleep Staging, Studies in health technology and informatics, 2016, Vol. 228, pp. 514–518.

Rospocher M., Serafini L. An Ontological Framework for Decision Support, Part of the Lecture Notes in Computer Science book series, Semantic Technology : Second Joint International Conference, JIST 2012, Nara, Japan, December 2–4, 2012 : proceedings. Nara, Springer, 2012, Vol. 7774, pp. 239–254.

Rospocher M., Serafini L. Ontology-centric decision support, Semantic Technologies Meet Recommender Systems & Big Data, 2012, Vol. 919, pp. 61–72.

Sutton R. S., Barto A. G. Reinforcement Learning: An Introduction. Cambridge, Massachusetts London, England : A Bradford Book, The MIT Press, 2012, 320 р.

Otterlo van M., Wiering M. Reinforcement learning and markov decision processes, Reinforcement Learning. Berlin, Springer, 2012, pp. 3–42.

Chen J., D. Dosyn, V. Lytvyn, A. SachenkoSmart Data Integration by Goal Driven Ontology Learning, Advances in Big Data, 2016, pp. 283–292.

Wong W., Liu W., Bennamoun M. Ontology learning from text: A look back and into the future, ACM Computing Surveys

(CSUR), 2012, Vol. 44(4):20, pp. 1–36.

Lytvyn V., Vysotska V., Pukach P., Bobyk І., Pakholok B. A

method for constructing recruitment rules based on the analysis of a specialist’s competences, Eastern-European Journal of Enterprise Technologies, 2016, Vol. 6/2(84), pp. 4–14. 19. Montes-y-Gómez M. Gelbukh A., López-López A. Comparison of Conceptual Graphs, Artificial Intelligence, Vol. 1793, 2000, pp. 548–556.

Su J., Vysotska V., Sachenko A., Lytvyn V., Burov Y. Information resources processing using linguistic analysis of textual content, Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 9th IEEE International Conference, 2017, pp. 573–578.

Lytvyn V., Vysotska V., Veres O., Rishnyak I., Rishnyak H.

Classification Methods of Text Documents Using Ontology

Based Approach, Advances in Intelligent Systems and Computing. Springer, 2017, Vol. 512, pp. 229–240.

Lytvyn V., Pukach P., Bobyk І., Vysotska V. The method of

formation of the status of personality understanding based on

the content analysis, Eastern-European Journal of Enterprise

Technologies, 2016, Vol. 5/2(83), pp. 4–12.

Lytvyn V., Vysotska V., Veres O., Rishnyak I., Rishnyak H.

Content Linguistic Analysis Methods for Textual Documents

Classification, Computer Science and Information Technologies: Proc. of the XI-th Int. Conf. (CSIT’2016), 2016, pp. 190–192.

Bisikalo O. V., Vysotska V. A. Identifying keywords on the

basis of content monitoring method in ukrainian texts, Radio

Electronics, Computer Science, Control, Vol. 1(36), 2016,

pp. 74–83.

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