Issue 2 (192), article 2

DOI:https://doi.org/10.15407/kvt192.02.023

Kibern. vyčisl. teh., 2018, Issue 2 (192), pp.

Grytsenko V.I.1,
Corresponding Member of NAS of Ukraine,
Director of International Research and Training
Center for Information Technologies and Systems
of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine
e-mail: vig@irtc.org.ua
Gladun A.Y.1,
PhD (Engineering), Senior Researcher of the Department of Complex Research
of Information Technologies and Systems
e-mail: glanat@yahoo.com
Rogushina Y.V.2,
PhD (Phyz&Math), Senior Researcher of the Department of Automated Information Systems
e-mail: ladanandraka2010@gmail.com
1International Research and Training Center for Information
Technologies and Systems of the National Academy
of Sciences of Ukraine and Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., 03187, Kiev, Ukraine
2Institute of Program Systems of the National Academy of Sciences of Ukraine,
40, Acad. Glushkov av., 03187, Kiev, Ukraine

MODELS AND METHODS OF THE SEMANTIC WIKI RESOURCES USE AS KNOWLEDGE SOURCES FOR RENEWAL OF FORMAL DOMEN ONTOLOGIES

Introduction. The construction and implementation of intelligent systems based on the formalization and reuse of knowledge is a promising direction for the practical application of artificial intelligence methods. The basis of such systems is formalized representations of knowledge about the subject area, for example, in the form of ontology. There remains an open question of the choice of the formal apparatus tools for the construction of ontology.
The purpose of the paper is to develop models of structured representation of knowledge in Wiki-resources on the basis of ontologies and methods of their application for improving and replenishing ontologies of the subject area. The offered approach will allow integrating the current information on changes in the subject area and creating actual ontologies for various applied information technologies using ontologies.
Results. The expediency of using ontologies for presentation of knowledge in systems of artificial intelligence oriented to functioning in the open environment of the Web is considered. The researches connected with the construction of formal ontologies of subject areas and the means of their formalization are analyzed. A formal model of ontology, which specifies the existing approaches, describing in more detail the properties and characteristics of the relations between the main elements of ontology is proposed. An example of using the proposed method in the task of transforming the natural text into a sign language in the system of information support of people with speech and hearing impairments is given.
Conclusions. The paper describes a method for renewal the ontology of a subject area based on the proposed model and the use of semantically-tagged Wiki-resources as a source of knowledge. This provides a dynamic replenishment of the knowledge base of applied intelligent systems. The proposed method of renewal formal ontologies of the subject domain from semantic Wiki-resources provides the expansion of the vocabulary and the construction of its specialized versions for various professional fields or subject areas using external databases. The automatic addition of new words from subject areas is particularly important for developing industries, especially for the IT sector, which has a large number of people with speech and hearing impairments. The proposed approach will improve the quality of life for many people, expanding the boundaries of their communication.

Keywords: formal ontology, ontological languages, formal model of ontology, interpretation of ontologies, semantic Wiki-resources, information system.

Download full text!

REFERENCES

1 Gruber, T.R. A translation approach to portable ontology specifications. Knowledge Acquisition. 1993. Vol 5. P. 199–220. https://doi.org/10.1006/knac.1993.1008

2 Poli R. Decriptive, Formal and Formalized Ontology. In: Husserl’s Logical Investigations reconsidered, by Denis Fisette (ed.). Dordrecht: Kluwer Academic Publishers, 2003, pp. 183–210. 24p. https://www.ontology.co/essays/descriptive-ontologies.pdf

3 Degen W., Heller B., Herre H., Smith B. GOL: A General Ontological Language. 2002, 63 p. https://www.researchgate.net/publication/2498727_GOL_A_General_Ontological_Language

4 Genesereth V.R., Fikes R.E. Knowledge Interchange Format, Version 3.0, Reference Manual. Logic Group Report Logic-92-1, Computer Science Department, Stanford University. 1992. 68p. https://pdfs.semanticscholar.org/b20c/866025b85f165557235a68143c42d53fa70f.pdf

5 Guarino N. Formal Ontology and Information Systems. In: Formal Ontology in Information Systems. Proceedings of FOIS’98, by N. Guarino (ed.). Trento. Italy, Amsterdam, IOS-Press. 1998. P. 3–15. https://klevas.mif.vu.lt/~donatas/Vadovavimas/Temos/OntologiskaiTeisingasKoncepcinisModeliavi-mas/papildoma/Guarino98-Formal%20Ontology%20and%20Information%20Systems.pdf

6 Vasyukov V.L. Formal ontology and artificial intelligence (monograph). Moscow: IF RAS, 2006. 140 p. (in Russian)

7 Rogushina Yu.V., Gladun A.Ya. Mereological aspects of the ontological analysis of intelligent Web-services. Proceedings of the VII International Conference “Intellectual Analysis of Information” IAI-2007, 12-14 Mai 2007, Kyiv. — P. 312–321. (in Russian)

8 Kotarbi!ǹ!sky T. Elements of the theory of cognition, formal logic and methodology of sciences. Lviv, 1929. 232p.

9 9. Wille R., Ganter B. Formale Begriffsanalyse. Berlin-Heidelberg: Springer-Verlag, 1996, 192 p.

10 Ganter B., Stumme G., Wille R., eds. Formal Concept Analysis: Foundation and Application. Lecture Notes in Artificial Intelligence. 2005, No. 3626. 349 p. https://link.springer.com/content/pdf/bfm%3A978-3-540-31881-1%2F1.pdf

11 Lammari N., Metais E. Building and maintaining ontologies: a set of algorithms. Data Knowledge Engineering, 2004, No. 48 (2), P 155–176.

12 Guarino N. Formal Ontology and Information Systems. In: Ontology in Information Systems. Proceedings of FOIS-08, Trento. Italy, by N. Guarino (ed.), Amsterdam, IOS-Press, 2009. 340 p. http://mba.eci.ufmg.br/downloads/recol/FormalOntologyinInforma-tionSystems2008.pdf

13 Rogushina Yu.V., Priyma S.M, Strokan O.V. Creating and using semantic Wiki-resources: tutorial. Melitopol, FOP Odnorog T.V., 2017. 169 p. (in Ukrainian).

14 Anisimov A.V., Lyman K.S., Marchenko A.A. Methods for computing measures of semantic proximity of natural language words. Artificial Intelligence, 2009, No 3. P. 612–617. (in Russian)

15 15. Lozynska O., Davydov M. Information technology for Ukrainian Sign Language translation based on ontologies. An International quarterly journal ECONTECHMOD, 2015, Vol. 04, No. 2, P. 13–18.

16 16. Gladun A.Ya. Khala K.O. Standard DSTU ISO/IEC 2382:2018 Information Technologies. Dictionary. Kyiv: UkrNDNC, 2018. 526 p. (in Ukrainian).

17 Anisimov A., Marchenko O., Taranukha V., Vozniuk T. Semantic and Syntactic Model of Natural Language Based on Non-negative Matrix and Tensor Factorization. Proceedings of the International Conference on Natural Language Processing, 2014, Springer, Cham. P. 177–184.

18 Gladun A.Ya., Rogushina Yu.V. Ontology Repository as a Tool for Reusing Knowledge for Recognizing Information Objects. Ontology of Design, No 1, 2013. P. 35–49. (in Russian).

19 Gladun A.Ya., Rogushina Yu.V. Semantic Technologies: Principles and Practices. Kyiv: Universarium, 2016. 387 p. (in Ukrainian).

20 Gladun A.Ya., Rogushina Yu.V. Bases of Methodology of Formation of Thesauruses with Use Ontologic and the Mereologic Analysis. Artificial Intelligence, 2008, No 5. P. 112–124. (in Ukrainian).

Received 02.04.2018